CA2331500A1 - Implantable medical device for tracking patient functional status - Google Patents
Implantable medical device for tracking patient functional status Download PDFInfo
- Publication number
- CA2331500A1 CA2331500A1 CA002331500A CA2331500A CA2331500A1 CA 2331500 A1 CA2331500 A1 CA 2331500A1 CA 002331500 A CA002331500 A CA 002331500A CA 2331500 A CA2331500 A CA 2331500A CA 2331500 A1 CA2331500 A1 CA 2331500A1
- Authority
- CA
- Canada
- Prior art keywords
- display
- activity
- values
- value
- patient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0031—Implanted circuitry
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
Abstract
An implantable medical device (71a, 71g, 71c) determines activity levels ove r a set of time periods, preferably on the order of seconds, minutes and hours and a display (82) is enabled for days or weeks at recorded activity levels over a range of dates. This enables physician review of patient functional status. Additional physiologic data can be recorded along with the activity data, and this too may be reported out from the implanted device to a medica l communications system for alarm purposes, titrating drugs or other monitorin g tasks.
Description
IMPLANTABLE MEDICAL DEVICE FOR TRACKING PATIENT FUNCTIONAL STATUS
Background.
There are numerous devices both implantable and external that have been used to monitor various medical patient conditions. Well known for heart patients is the Holter monitor which permits somewhat uncomfortable monitoring of an electrocardiogram for 24 hours which can then be read by a physician to find 1o anamolies in the rhythm which were not susceptible to discovery or confirmation in a patient's office visit to the doctor. A number of other devices have improved on the ability to maintain records of electrocardiograms and numerous other health related patient parameters and even device performance parameters. Implantable medical devices such as pacemakers and cardioverter-defibrillators and even non-~5 therapeutic monitoring devices are currently capable of maintaining some records and reporting out such data. An example of a non-therapy delivering monitoring implantable medical device can be seen in US Patent Nos. 5,313,953 and 5,411,031 issued to Yomtov et al., and in Holsbach et al, 5,312,446, and others. Nolan et al,'s US Pat. No. 5,404,877 teaches that such devices can even generate patient 2o alarms. All these patents are incorporated herein by this reference in that they provide information about what can currently be done in the implantable device field.
Current generation pacemakers and implantabie defibrillators/cardioverters have the 25 ability to store different types of information in order to provide feedback to the clinician about the patient/device system. Examples of stored information include arrhythmia diagnostics, histograms of paced and sensed events, electrograms and trends of lead impedance. Such information is useful not only in optimizing device programming but also in the management of the patient's arrhythmias and other 3o conditions. While our invention focuses on the monitoring of patient activity, WO 99/58056 pCT/US99/10282 which we use as a functional status monitor, the additional information available from implantable devices could be used as an adjunct.
However, to date the literature is devoid of a satisfactory description of how to use activity information. There has been considerable thinking in this area, but none have yet succeeded in producing a satisfactory measure to track patient functional status. Some examples of this thinking in the current literature include:
Walsh J. T., Charlesworth A., Andrews R., Hawkins M., and Cowley A. J. "
1o Relation of daily activity levels in patients with chronic heart failure to long-term prognosis", Am J Cardiol, 1997, 79: 1364-1369.
Rankin S. L. , Brifa T. G. , Morton A. R. , and Hung J. , "A specific activity questionnaire to measure the functional capacity of cardiac patients", Am J
Cardiol 1996, 77: 1220-1223.
~s Davies S. W., Jordan S. L., and Lipkin D. P., "Use of limb movement sensors as indicators of the level of everyday physical activity in chronic congestive heart failure", Am J Cardiol 1992, 67: 1581-1586.
Hoodless D. J. , Stainer K. , Savic N. , Batin P. , Hawkins M. and Cowley A.
J. , "
2o Reduced customary activity in chronic heart failure: assessment with a new shoe-mounted pedometer", International Journal of Cardiology, 1994, 43: 39-42.
Alt E. , Matula M. , Theres H. , Heinz M. and Baker R. , " The basis for activity controlled rate variable cardiac pacemakers: An analysis of mechanical forces on 2s the human body induced by exercise and environment", PACE, vol 12, Oct, 1989.
Lau C. P., Mehta D., Toff W. D., Stott R. J., Ward D. E. and Camm A. J., "
Limitations of rate response of an activity sensing rate responsive pacemaker to 3o different forms of activity ", PACE, vol. 11, Feb 1988, and Lau C. P. , Stott J. R. R. , Zetlin M. B. , Ward A. J. , and Camm A. J. , "
Selective vibration sensing: a new concept for activity-sensing rate-responsive pacing", PACE, vol. 11, September, 1988. Matula M. , Schlegl M. , and Alt E. , "
Activity controlled cardiac pacemakers during stairwalking: A comparison of s accelerometer with vibration guided devices and with sinus rate", PACE, 1996, vol 19, 1036:1041.
Suecific information uses:
The ability to perform normal daily activities is an important indicator of a patient's to functional status and is related to improved quality of life in patients.
An increase in the ability to perform activities of daily living (ADL) is an indicator of improving health and functional status, while a decrease in the ability to perform daily activities may be an important indicator of worsening health. Activities of daily living are submaximal activities performed during daily life. Examples are going to 1s work, cleaning the house, vacuuming the house, cooking and cleaning, working in the garden, short walk to grocery stores, cleaning the car, and slow paced evening walks.
In order the assess the amount of daily activities that patients can perform and the 2o ease with which they can perform these activities, clinicians typically ask their patients during office visits the following questions:
~ How do you feel ?
~ Are you as active today as you were 2 months ago ?
~ Are you as active today as you were 6 months ago ?
2s ~ Are you able to climb stairs ?
~ How far can you walk ?
~ Do you do your own grocery shopping?
~ Do you perform chores around the house ?
~ Are you able to complete your activities without resting ?
Background.
There are numerous devices both implantable and external that have been used to monitor various medical patient conditions. Well known for heart patients is the Holter monitor which permits somewhat uncomfortable monitoring of an electrocardiogram for 24 hours which can then be read by a physician to find 1o anamolies in the rhythm which were not susceptible to discovery or confirmation in a patient's office visit to the doctor. A number of other devices have improved on the ability to maintain records of electrocardiograms and numerous other health related patient parameters and even device performance parameters. Implantable medical devices such as pacemakers and cardioverter-defibrillators and even non-~5 therapeutic monitoring devices are currently capable of maintaining some records and reporting out such data. An example of a non-therapy delivering monitoring implantable medical device can be seen in US Patent Nos. 5,313,953 and 5,411,031 issued to Yomtov et al., and in Holsbach et al, 5,312,446, and others. Nolan et al,'s US Pat. No. 5,404,877 teaches that such devices can even generate patient 2o alarms. All these patents are incorporated herein by this reference in that they provide information about what can currently be done in the implantable device field.
Current generation pacemakers and implantabie defibrillators/cardioverters have the 25 ability to store different types of information in order to provide feedback to the clinician about the patient/device system. Examples of stored information include arrhythmia diagnostics, histograms of paced and sensed events, electrograms and trends of lead impedance. Such information is useful not only in optimizing device programming but also in the management of the patient's arrhythmias and other 3o conditions. While our invention focuses on the monitoring of patient activity, WO 99/58056 pCT/US99/10282 which we use as a functional status monitor, the additional information available from implantable devices could be used as an adjunct.
However, to date the literature is devoid of a satisfactory description of how to use activity information. There has been considerable thinking in this area, but none have yet succeeded in producing a satisfactory measure to track patient functional status. Some examples of this thinking in the current literature include:
Walsh J. T., Charlesworth A., Andrews R., Hawkins M., and Cowley A. J. "
1o Relation of daily activity levels in patients with chronic heart failure to long-term prognosis", Am J Cardiol, 1997, 79: 1364-1369.
Rankin S. L. , Brifa T. G. , Morton A. R. , and Hung J. , "A specific activity questionnaire to measure the functional capacity of cardiac patients", Am J
Cardiol 1996, 77: 1220-1223.
~s Davies S. W., Jordan S. L., and Lipkin D. P., "Use of limb movement sensors as indicators of the level of everyday physical activity in chronic congestive heart failure", Am J Cardiol 1992, 67: 1581-1586.
Hoodless D. J. , Stainer K. , Savic N. , Batin P. , Hawkins M. and Cowley A.
J. , "
2o Reduced customary activity in chronic heart failure: assessment with a new shoe-mounted pedometer", International Journal of Cardiology, 1994, 43: 39-42.
Alt E. , Matula M. , Theres H. , Heinz M. and Baker R. , " The basis for activity controlled rate variable cardiac pacemakers: An analysis of mechanical forces on 2s the human body induced by exercise and environment", PACE, vol 12, Oct, 1989.
Lau C. P., Mehta D., Toff W. D., Stott R. J., Ward D. E. and Camm A. J., "
Limitations of rate response of an activity sensing rate responsive pacemaker to 3o different forms of activity ", PACE, vol. 11, Feb 1988, and Lau C. P. , Stott J. R. R. , Zetlin M. B. , Ward A. J. , and Camm A. J. , "
Selective vibration sensing: a new concept for activity-sensing rate-responsive pacing", PACE, vol. 11, September, 1988. Matula M. , Schlegl M. , and Alt E. , "
Activity controlled cardiac pacemakers during stairwalking: A comparison of s accelerometer with vibration guided devices and with sinus rate", PACE, 1996, vol 19, 1036:1041.
Suecific information uses:
The ability to perform normal daily activities is an important indicator of a patient's to functional status and is related to improved quality of life in patients.
An increase in the ability to perform activities of daily living (ADL) is an indicator of improving health and functional status, while a decrease in the ability to perform daily activities may be an important indicator of worsening health. Activities of daily living are submaximal activities performed during daily life. Examples are going to 1s work, cleaning the house, vacuuming the house, cooking and cleaning, working in the garden, short walk to grocery stores, cleaning the car, and slow paced evening walks.
In order the assess the amount of daily activities that patients can perform and the 2o ease with which they can perform these activities, clinicians typically ask their patients during office visits the following questions:
~ How do you feel ?
~ Are you as active today as you were 2 months ago ?
~ Are you as active today as you were 6 months ago ?
2s ~ Are you able to climb stairs ?
~ How far can you walk ?
~ Do you do your own grocery shopping?
~ Do you perform chores around the house ?
~ Are you able to complete your activities without resting ?
They also employ other tools such as the symptom based treadmill exercise test, the 6 minute walk test, questions and answers (Q&A) , and quality of life(QOL) questionnaires in order to learn about their patients' ability to perform exercise and normal activities, but these assessment tools have limitations. Q&A techniques are subjective and biased towards recent events(at least partly due to patient bias toward present recall, if not also due to patient memory impairment or insufficiency, or a patient's desire to provide positive data). Maximal treadmill exercise tests assesses the patient's ability to perform intense (maximal) exercises and do not reflect the 1o ability to perform normal daily activities. The 6 minute walk test has to be administered very carefully and rigorously to achieve valid results.
Impairment of functional status can be seen in changes in the ability to perform exercises and ADL. This can be affected by many physiological factors such as ~s progressive decompensation in the setting of left ventricular cardiac (LV) dysfunction, beta blocker treatment, symptomatic arrhythmias, and depression.
These changes may take place over a long period of time and may be too subtle to be discerned by patients.
2o Physicians use answers to these questions and observation in clinic to determine what New York Heart Association "class" into which a patient falls, and on this basis, among others, they administer and alter treatment. Class I is defined as "Patients with cardiac disease but without resulting limitation of physical activity.
Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea or 25 anginal pain. " NYHA Class II is "Patients with cardiac disease resulting in slight limitation of physical activity. They are comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea or anginal pain. ", Class III is defined: "Patients with marked limitation of physical activity. They are comfortable at rest. Less than ordinary activity causes fatigue, palpitation, dyspnea, 30 or anginal pain. And, Class IV is "Patients with cardiac disease resulting in inability to carry on any physical activity without discomfort. Symptoms of heart failure or of the anginal syndrome may be present even at rest. If any physical activity is undertaken, discomfort is increased. "
As implantable device technology advances, there is a further need to provide 5 information that will allow the clinician to not only manage arrhythmias better but also the progression of other diseases (co-morbidities) that patients may have. With the advent of newer drugs and newer paradigms in drug therapy (the use of beta blockers in heart failure patients is just one example), there is a need for objective measures of patient response. Several parameters such as ventricular pressure, patient activity, lung wetness, and heart rate variability may provide such information to the clinician.
In the management of patient care over a relatively long period of time, it is believed that current implantable devices with their larger memories and even using ~s some extant sensors may be enhanced to produce a set of data that indicates patient functional status on an on-going basis. For patients who do not have already a need for an implanted medical device as an adjunct to their medical therapy, the addition of a specialized implantable that has extremely limited capability and thus small size may provide an additional tool for medical management of disease, particularly 2o Cardiac Heart Failure (CHF).
However, it seems that the simplest and possibly most accurate measurement which can determine the prognosis and progress of a patient has not been previously monitored, and further this indicator has not been monitored in a manner effective 2s to elucidate for the physician the changing character of the patient's CHF
disease progression.
If there were a simple and yet effective measure that could be reliably correlated with the progress of CHF, the use of other hemodynamic measures could be used to 3o supplement it and could easily be added to an implantable device. This indication alone may provide a sufficient justification for implantation of a device. In other words, if a very inexpensive implantable device could be developed to chronically monitor a simple indicator of CHF prognosis, the care available to CHF
patients could be improved by using this data. Administration of patient care based on this inexpensive implant's data would improve the lives of CHF patients by virtue of their needing less frequent clinic visits for drug titration and other observationally intense activities, since the status of the patient could be determined without resort to an expensive doctor visit by merely and viewing the data from these CHF
status indicators, the drugs themselves could be adjusted, alarms could be sent, and other therapies automatically adjusted based on this status report. It is believed that the ~o common usage of such systems awaits the development of an information resource such as is taught in this patent.
We have determined that a long term trend of physical activity in CHF patients may thus provide the clinician with an objective measure of the patient's life-style and 1s functional status, and can be used in conjunction with other information, but as explained previously, an objective long term measure is currently unavailable from implantable medical devices. Having available a display of a trend of patient ability to perform ADL is useful in several situations.
2o Correlation of physical activity with patient testimony.
Clinicians often encounter patients who find it difficult to verbalize their symptoms clearly. In such situations, an objective measure of patient activity stored in the device may help the clinician to decide an appropriate course of action. For 25 example, if a patient complains vaguely of fatigue and shortness of breath and is not able to describe the limitations to his/her daily activities, a trend of activities may help the clinician. If the activity data could show a considerable decrease in patient activities, then the clinician may take the next step such as the evaluation of cardiac profile, pulmonary dysfunction, or existing drug therapy. On the other hand, if the 30 long term trend of activity in this patient is consistently regular, (i.e., no decrease in patient activity), the clinician may take alternative steps to understand the difference between patient symptoms and device indicated activity data.
Clinicians may also encounter situations when patients are reluctant to discuss their s symptoms. In situations such as these, a trend of activity data may help the clinician to question the patient or the patient's spouse and enable the patient to come forward with their symptoms Correlation of physical activity with onset or progression of Heart Failure.
io Heart Failure is a syndrome characterized by the coexistence of left ventricular dysfunction (low EF), arrhythmias, pulmonary and peripheral congestion, and symptoms of fatigue and shortness breath. A majority of ICD patients have low EF
( < 40~) and decreased functional capacity (NYHA Class II, III and IV), and are at risk of developing heart failure. Clinical heart failure is a progressive disease;
is hence early identification and timely therapy may prevent hospitalizations, reduce the cost of care and improve patient lives.
In the earlier stages of heart failure, patients may not be able to perform strenuous activities and in the later stages, may not be able to perform even routine activities 2o such as walking up a flight of stairs. Further, the inability to perform exercise and activities develop over a long period of time and hence may be difficult to discern and quantify. An objective measure of long term trends of patient activity may be useful in early identification of symptoms of heart failure and in the progression of heart failure. A gradual decrease in patient activities over the last 8 months may 2s lead the clinician to suspect the development of heart failure. This may lead the clinician to take the next step in differential diagnosis.
The ability to perform daily activities is of particular relevance to the onset and progression of heart failure. Several studies in the literature have described the need 3o for an objective measurement of activities of daily living in patients with heart failure. This is no doubt why the NYHA measures focus so much on this ability to perform daily activities.
Correlation of physical activity with patient response to therapy.
s The use of beta-blockers in patients at risk of arrhythmias or heart failure is becoming common clinical practice. Beta-blocking agents are known to blunt the heart rate response, which may affect the patient's ability to perform certain activities. The optimal dosage of beta-blocking agents is difficult to predict and may require trial and error methods. Further, there is usually a 30-60 day time period 1o immediately after initiation of drug therapy during which the patient may not be very active. Most patients acclimate to the therapy after this period, but some don't.
By having an objective measurement of patient activities, the titration and adjustment to beta blockers and other drugs could be enhanced.
Correlation of physical activity with arrhythmias.
~s Arrhythmias may cause symptoms such as palpitations, fatigue, or presyncope.
Some patients may spend a significant amount of time in arrhythmias such as atrial fibrillation and may not be able to perform daily activities. Such issues can be coordinated with measurements of activity for increased diagnostic value.
As is known in the art, implantable medical devices exist that have various accelerometers and piezocrystal activity sensors and the like which count the movement of the crystal or sensor with respect to a resting state. Medtronic brand implantable medical devices with piezoelectric crystal or accelerometer based activity sensors have the ability to convert a raw activity signal into the 2 second activity counts. In other words, the number of times the accelerometer or sensor moves in a two second period is called a 2 second activity count. However, nothing in the art describes a method or apparatus for succinctly and effectively compiling such data as activity counts to make that data effective to solve the problems in diagnosis and patient tracking described above.
What is needed is a device with a system to convert these activity counts or some equivalent of them into a measure of patient activity that is clinically meaningful.
To review the specifics of the problem consider the raw signal. The raw activity signal and hence the processed activity counts is a result of vibrations due to body movement. Activities such as walking and running cause body movement and 1o vibration; the faster or longer the walk, more the vibrations, and larger the activity signal. Even though the raw activity signal is a good measure of activities such as walking and running, these raw counts, without our invention do not provide an accurate assessment of patient physical exertion. This is for three classes of reasons.
1. Body vibrations are not always proportional to level of exertion.
Any activity that causes body movement such as walking and running will generate an activity signal. Studies have shown that the amplitude of the activity 2o counts increases in a linear fashion for walking activities. However, this linear relationship between intensity of activity and the activity signal during walking does not apply to all activities. For example, walking up-stairs at the same speed will produce activity counts similar to walking on a flat surface even though the intensity of activity is higher while walking upstairs. Other examples are isometric exercise, stationary bike, and jogging in place. Since the activity counts are derived from this activity signal, they may not be an objective measure of patients' exertion levels for all types of activity. In fact even a simple activity such as walking may produce different activity counts depending on the human-ground interface, for example, walking on a carpeted surface versus an asphalt surface.
2. Lack of specificity.
Activities such as automobile driving that result in body vibrations but do not involve exertion may sometimes produce an activity signal that may be comparable in amplitude to the level of the activity signal during walking. Likewise, the s orientation of an accelerometer may not pick up an activity like push ups, despite the large exertion.
3. Inter-person variability.
1o There is also inter-person variability for similar activities. Differing size and fat content of the patient body, as well as placement of the device in various locations and orientations will all contribute to this kind of variability.
Based on these considerations, it is clear that in order to convert the activity counts to a meaningful measure of patient activity, the system must 1. be sensitive enough to pick up low-level activities (activities of daily living) as well as high-level exertion activities, 2. minimize response to activities such as automobile driving, 3. be patient independent, i.e., it should not require a user programmable parameter and should 4. be easy to implement in an implantable device Brief Description of the Drawings.
Figs. 1-3 are graphs of time versus activity counts over time in accord with preferred embodiments of the invention, used to describe various changes in patient 3o condition over time.
Figs. 4a-c are similar graphs of activity versus time.
Figs. Sa and Sb are graphs of atrial fibrillation versus time and activity versus time using the same time axis.
Fig. 6 is a graph indicating a NYHA class level with patient activity over time.
Fig. 7 is a drawing of the inside of a patient's body having three implants within it on the right side of a line BL and an external programmer for communication with 1o the implantable devices.
Summary of the invention.
This invention provides a patient activity monitor for chronic implant which is provides well correlated evidence of the functional status of the patient with the implant. It does so by monitoring an activity signal related to the movement of the patient, and from this monitored movement determining a level of activity indication signal value.
2o Detailed description of the preferred embodiments Nearly any currently implanted medical devices could be adapted to employ the features of this invention provided only that such a device maintains either a direct and constant link with a memory device or has its own memory device and its own activity sensor, and that there is provided an appropriate processing circuit or 25 program to enable the invention activity. Activity sensors are well known and have been employed in pacemaker type implantable medical devices for many years. A
typical such device is seen in Strandberg's US patent No. 4,886,064, and it is now common to see the basic activity sensor combined with alternative means for sensing activity such as minute ventilation as in US Patent No. 5,562,711, both of 3o which are hereby incorporated by this reference in their entireties.
Referring now to Fig. 7, in which a set of alternative implantable devices 71a-c are illustrated inside of a patient body, (the edge of which is here illustrated as line BL,) the typical application of this invention will be to provide data for a display 82 on an external device such as a programmer P external to the patient body, via a conununications channel such as is here represented by double headed arrow C.
The data may be shown in the form of a bar chart or line graph or similar display which indicates the total amount of some algorithmically derived measure of activity over a given period of time, such as a.day or an hour. Device 71a is a pacemaker having a memory 75a which stores the data measured by the sensor 76a. The storage can be in a raw form if there is sufficient memory or it can be compressed in various advantageous forms by a program or other circuit device running a process such as processor 77a. In this embodiment, the microprocessor 77a runs a program stored in memory to convert sensed activity counts processed through an analog to digital converter 79 as they appear on a bus 78, and then returns the 1s processed data to the program for temporary storage in the memory circuit 75a.
When enough measurements are made in accord with the program, the microprocessor converts a representation of the total to a value and stores the representation in the memory. When an external device such as a programmer P
requests a dump of these stored representations of value indicating the amount of 2o patient activity over time, it is formatted and sent over the communications channel by a communications circuit 83 to the external device so that it can be displayed in a human readable form. Alternatively, of course, the data can be sent via communications here simply represented by arrow C2 and arrow L1 to be stored in a temporary device TD for later relaying to other devices by phone or othe 25 rtelemetry, here represented simply by line L2, for later or contemporaneous but distant display. Only the simplest construction required for operative use of the invention is shown here. It is a simple matter to resend data over modern communications systems once it is retrieved in a machine readable format. As most implantable cardioverter defibrillators and pacemakers of today have 3o microprocessors, memories and activity sensors, the addition of this invention to such devices would require no additional hardware, but mere software reconfiguration to accommodate the requirements of storing appropriate activity data in a useful manner so as a to be available for use in accord with this invention.
Also, alternative forms of implant devices can be used. The Medtronic device REVEAL(TM), for example is fully implantable and similar to the device illustrated as device 71 c, with a memory and processing circuitry for storing electrocardiograms, taken across two electrodes 79 and 72c. The addition of an additional circuit for sensing activity and appropriate circuitry to implement the storage of the relevant activity data in an appropriate manner would make this type of device another good candidate for the inventive features herein described.
Also a drug pump such as device 71b when outfitted with the appropriate memory, processing and sensing circuits could do the same, as could other currently implantable devices. It should be noted that a device with nothing but an activity sensor, memory, a processor and some form of communications circuit would also be sufficient to perform the tasks required of this invention's implantable device.
By using a kinetic power source, the dependence on a battery could be eliminated too and the device could be extremely small and unobtrusive, permitting the clinician to easily obtain patient consent to accept the implant.
2o Once the valuable functional status data is available, any communications system could be employed to get the information to the doctor, or to update the patient file.
Any such uses of the information provided by this invention are contemplated.
Fig. 1 is a prototype illustration (using simulated data) over a 14 month period of 2s patient activity data. Note that the patient can be seen over a very long period and any changes in the activity amount will be readily visible. Also, as illustrated here, even fairly large variations in daily activity will not influence the overall impression of health. Here the graph 10 displays hours in which the activity count was in the active range per day 12 versus time 13 on the line 11. In Fig. 2, the activity line 15 3o begins trending downward in late September confirming that the patient should be seen at the time shown 16.
Figure 3 illustrates a long term trend of patient activity data. This situation may be that of a post myocardial infarction (MI), sudden cardiac death survivor with EF <
40 % .
In the Figs. 4a-c are prototypical illustrated activity trends for patients with a drug therapy intervention. In case 1, Fig 4a, graph 20a, line 21a shown little or no effect of the drug regimen. the patient report at the time he is seen will probably indicate no change in status, based on the height of curve 21a. In case 20b, the to drug administration has an apparent effect by the time the patient is seen, which in graph 20c, the line 21c shown no effect of the Beta blocker administration.
Thus, objective measurement of patient activities as illustrated in Figures 4a-c can provide feedback to the clinician during the use of beta blocker therapy. Figure 4a is a scenario where activities are not decreased. Fig. 4b illustrates a temporary decrease ~5 in patient activities followed by an increase to the original level. Fig.
4c shows that patient activities decreased and stay decreased, and may require the modification to the drug dosage.
Figs 5 a and Sb show how the activity data can be coordinated with a display of 20 other patient related data, here hours in atrial fibrillation per day. Note the apparent correlation with the height of the line 21d and the increased occurrence of Atrial Fibrillation as would be expected. A device such as 71c which could track both fibrillation's and activity could be used to produce such a paired graph display. The ability to correlate the duration of arrhythmia episodes with patient activity as 2s shown in Figure 5 may help the physician in treating the arrhythmias. For example, if the patient is being monitored with a device that can display the occurrence of an arrhythmia or an accumulation of arrhythmic events over time as shown in this figure, or if such a feature is incorporated into the inventive device, the physician can learn immediately whether the arrhythmia occurs with activity, an 3o important piece of diagnostic information.
Figure 6 illustrates correspondence with physician determined NYHA Class level of a CHF patient over a relatively short time period with his activity level in hours along the vertical axis. This chart shows the NYHA Classification and activities per day for the 7 follow-ups. As can be seen in the figure, activities per day appear to 5 be increasing over time in correlation with the improvement in NYHA
functional Classification from Class III to Class I/II.
In order to explain how the compilation of useful information from the raw data can be accomplished, refer first to Fig. 8 in which activity counts from a normal subject collected over a 24 hour period is shown.
These signals can be collected from pacemakers which have been using piezoelectric crystals and accelerometers mounted inside the pacemaker can as an indicator of patient activity to control the rate of the pacemaker. Typically, the raw 1s acceierometer/crystal signal is first filtered using a bandpass filter and the total number of crossings above and below a fixed threshold is calculated every 2 seconds. The rate of the pacemaker is then calculated based on these 2 second activity counts. These 2 second activity counts can also be used to acquire information regarding patient activity. For heart rate and Heart Rate 2o Variability(HRV), most pacemaker devices already have the ability to calculate heartbeat intervals and changes in these calculated variables over time gives the HRV. Other measures such as breathing rate, oxygen saturation, blood pressure, temperature, or just about any other measurement that can be made could be coordinated with the activity display to provide useful information, but we only show HRV here to teach that such can be easily done, not to limit the invention to this one coordination display.
These observations are evidence that the concept of using the accelerometer signal to differentiate between activities and non-activities is appropriate and acceptable.
Description of candidate algorithms for implementing this- invention~
Based on the concerns just described we selected 5 candidate algorithms for determining a value for activity level per day, and we tested these on a large set of 24 hour data. A description of the 5 algorithms follow. These are examples only.
We ultimately selected as a most preferred algorithm one which uses 60second periods rather than 80, to provide easier conversion to minutes and hours. We also found that in producing a display it was sensible to average the daily values for a week, since normal activity cycles over a week can vary significantly over a weekend, particularly.
ADLl: First, the average activity count over 80seconds is calculated by adding consecutive 2 second counts. The average is then compared with a threshold of 1.5.
If the average is greater than or equal to 1.5, then the 80s period is considered as activity (ADL), and the average for the next 80s time period is calculated. At the end of the 24 hour data, total activity duration is calculated by adding the number of 80 second windows that were detected as ADL, and multiplying with 80.
ADL2: This algorithm is similar to ADL1 except for the choice of threshold (2.0).
This choice was made to evaluate the trade-off between detecting true ADL and 2o driving, a lower threshold will detect most ADL as well as driving while a higher threshold will detect lesser driving and true ADL.
ADL3: This algorithm is similar to ADL2 except for the choice of window. Since we do not have any a priori knowledge of the typical duration of activities of daily living, a choice of 40 seconds was made in order to study the difference in 25 performance between the 80 second and the 40 second windows.
ADL4: As described above, ADLl, ADL2 and ADL3 are threshold algorithms and do not attempt to separate out non-activities from activities based on variability (only amplitude information is used). In the ADL4 algorithm, the 80 second average is first calculated. The number of 2 second counts that are greater than 0 3o are also noted. If the 80 second average is greater than 1.0 and at least second counts in the 80 second window are greater than 0, then the 80 second window is detected as ADL.
ADLS: This algorithm is another way to use the variability information. The 80 second window is separated into 20 second sub-windows and the average for the s second window as well as the 4 sub-windows are calculated. If the average of the 80 second window is greater than 1.0 and 3/4 sub-window averages are greater than 0.3 *(average of 80 second window), then the 80 second window is detected as ADL.
t o Algorithm Evaluation The 24 hour data set we used was collected from 10 normal subjects and separated into two groups, a Development data set and a Validation data set. Three performance measures P 1, P2 and P3 were calculated for each data set in the ~5 Development data set. Algorithm parameters were adjusted to achieve the highest P1 and P2, and lowest P3. Since these performance measures are different from the traditional sensitivity and specificity measures used to evaluate algorithms, each of these measures is defined and explained below.
Performance measure P1 PI =
Duration of marked ADL that is detected by al orithm) Duration of marked ADL as marked by subject (gold standard) We had the normal subjects mark those times they felt they were active. Upon close inspection of the marked ADL, it was found that several marked ADL
events were of the stop-go-stop-go type, i.e., there were periods of rest in between short bouts of activity, which is characteristic of activities of daily living. In order not to 3o penalize the algorithm for the detection of rest periods in between marked ADLs, only that part of marked ADL that was associated with an elevated heart rate ( heart rate during rest period during day + 10 bpm) was considered as marked ADL.
This procedure can be thought of as refining the gold standard data.
s Performance measure P2 This measure was used to ascertain whether the algorithm was detecting non-activities as ADL. Since not all activities and non-activities are marked, a heart rate based criterion was used to differentiate between activities and non-activities.
Specifically, activities that were associated with a elevated heart rate (resting heart rate during day + 10 bpm).
P2 =
Total time detected as ADL associated with a elevated heart rate (P2N) is Total time detected as ADL (P2D) For example, to calculate P2 from a 24 hour data set, P2D is calculated as the # of 80 second activity windows detected as ADL by the algorithm. To calculate P2N, 2o the heart rate in the 80 second window corresponding to the 80 second activity window that is detected as an ADL is calculated first. If the heart rate in this window is above (rest heart rate + 10 bpm), then the 80 second activity window is said to be appropriately detected. The ratio of P2N and P2D is the performance measure P2.
2s Performance measure P3 This measure is used to ascertain the ability of the algorithm to eliminate detection of automobile driving as ADL. Automobile driving (or riding in a bumpy 3o conveyance of any type) we believe will be one of the main causes of false positives. This measure P3 is calculated as the ratio of P3N and P3D, where P3D is the total duration of marked driving by the normal subject and P3N is the duration during which marked driving was detected as ADL. Clearly, the lower P3 is, the better the algorithm.
P3 =
_ Total driving time that is detected as ADL (P3N) Total marked driving time (P3D) Calculation of heart rate To obtain heart rate data, 24 hour surface ECG data stored in a Holter Monitor system were downloaded and analyzed using "Hotter for Windows"(TM) software (available through Rozin Electronics, Glendale NY). Every beat of the ECG data (each beat corresponds to the detection of a QRS complex) was classified as a Normal beat, Ventricular beat, Supra-ventricular beat or an Artifact. Only intervals ~5 between normal (sinus) beats (referred to as NN intervals) were used to compute heart rates during the entire 24 hour period.
The accelerometer signal is processed and a raw count is calculated every 2 seconds (ACTCNT) for pacemaker rate response. An algorithm to calculate minutes of ADL
2o from ACTCNT is as follows:
[Where NUM is the total number of activity counts, THRESH is the threshold for whether the activity count value will be counted as a one or a zero in the SUM
of counts, and DAILYCOUNT is the variable value to be displayed for a single day.
It should be noted that if the values of a week of DAILYCOUNTs are averaged to 2s provide a single point to display as was the case for the graphs of Figs. 1-5, the average value will be what is displayed, but for more detailed analysis, even if averaging is used, the DAILYCOUNT values would preferably be retained beyond a given week, unless the device in question is operating with minimal memory capacity.
Step i . Starting at 12 a. m. (00:00:00), add NUM ACTCNT, i.e., SUM = S ACTCNT/ NUM.
s Step 2. If SUM > THRESH, increment a counter (DAILYCOUNT) else next step.
Step 3. Repeat Step 1 and 2 continuously till the next 12 midnight Step 4. Save DAILYCOUNT for this day and repeat Steps 1-4 to If averaging over a larger period, average the DAILY COUNT values for the past number of days in the period and establish a larger value representing the average, AVDAILYCOUNT for the period, so that that value AVDAILYCOUNT can be displayed.
NUM and THRESH are 1 byte programmable Recommended values are NUM = 30, THRESH = 45 2o Memory requirements:
For NUM = 30, DAILYCOUNT could have a maximum value of 1440 and would require 2 bytes of storage every day.
Total memory: 2 bytes a day (850 bytes for 425 days) Software algorithm and printing Step 1. ADLDAY = DAILYCOUNT * NUM * 2 / 60 3o Step 2. ADL= Avg. ( 7 consecutive ADLDAY ) Graph ADL every week as illustrated in "Long Term Clinical Trends "
Y axis title: "Total hours of patient activity per day"
Y axis tick labels: " 0 2 4 > 6 " Hours s Algorithm Evaluation As detailed above, each of the 5 candidate algorithms were applied to each of the 5 data sets in the Development data set and the three performance measures Pl, and P3 calculated. The algorithm parameters were changed to maximize P1 and P2 1 o and minimize P3. The values and brief descriptions of Pl , P2 and P3 for each of these 5 normals is shown in Table 2. Following algorithm development, the algorithms were applied to the Validation data set and the process repeated.
Table 3 shows the performance measures for the Validation data set. It is important to note that the utility of the performance measures is limited to the comparison of the 1s different algorithms only and is not meant to be used as an absolute measure of sensitivity and specificity.
As detailed in the Methods section, each of the 5 candidate algorithms were applied to each of the 5 data sets in the Development data set and the three performance 2o measures P 1, P2 and P3 calculated. The algorithm parameters were changed to maximize P1 and P2 and minimize P3. The values and brief descriptions of P1, and P3 for each of these 5 normals is shown in Table 2. Following algorithm development, the algorithms were applied to the Validation data set and the process repeated. Table 3 shows the performance measures for the Validation data set.
It is 2s important to note that the utility of the performance measures is limited to the comparison of the different algorithms only and is not meant to be used as an absolute measure of sensitivity and specificity.
pl ~ P3 ADL1 70 % 86 % 28 %
ADL2 62 % 91 % 20 ADL3 60 % 89 % - 20 ADL4 72 % 86 % 42 %
ADLS 65 % 87 % 36 Pl: Ability of the algorithm to detect marked ADL activities P2: Ability of the algorithm to detect activities that are associated with an increase in heart rate P3: Ability of the algorithm to detect automobile driving.
Table 2. Performance measures for Development data set pl pZ P3 ADLl 72 % 80 % 24 %
ADL2 66 % 85 % 14 %
ADL3 63 % 84 % 14 ADL4 74% $0% 36%
ADLS 66 % g0 % 28 1o Table 3. Performance measures for Validation data set Several observations can be made from these results. Given that the goal of the algorithm is to maximizx P1 and P2 and minimize P3, it is clear from Table 3 that algorithm ADL1 and ADL2 best meet the criteria. The only difference between ADL1 and ADL2 is the threshold parameter, ADL1 had a threshold of 1.5 and s ADL2 had a threshold of 2Ø It is to be expected that a higher threshold (ADL2) would lead to a decreased sensitivity (P1 is lower for ADL2) and but increased specificity (P2 is higher for ADL2). The only difference between ADL2 and ADL3 is the choice of window (ADL2 used a 80s window while ADL3 used a 40s window). Based on the results, it appears that decreasing the window from 80s to 40s had a minimal effect on the performance measures. This may be because most ADL may have been longer than 40 seconds. The variability algorithms do not appear to detect driving any less than the threshold type algorithms. This may be because of the fact that the variability of driving while evident in the 2 second data may not be a factor when data is averaged over 80 seconds. Table 3 shows the ~s . ~ performance for the Validation data set. The results from the Validation data set are consistent with the observations made from the Development data set.
Based on these observations, ADL1 (essentially same as ADL2) algorithm was chosen as the algorithm of choice since this algorithm has the highest sensitivity for 2o marked ADL activities. Coincidentally, this algorithm is associated with the least implementation complexity.
Comparison of ADL between normals and heart failure patients As explained in the Data Collection section, 24 hour activity and heart rate data 2s were collected from 10 heart failure patients as part of the EXACT study in addition to the 10 normal subjects as part of the Normal study. Such data were collected 7 times from each patient over a period of 16 weeks. However, detailed diary data were not collected from the patients as was from the normal subjects. Even though the 24 hour EXACT data from the patients do not lend themselves to the type of analysis performed on the data from normal subjects, these data were used to study algorithm performance in two different ways.
Thirty (30) data sets, each consisting of 24 hour heart rate and activity data from a s patient were analyzed using algorithm ADL1. At the beginning of the 24 hour follow-up, each patient undergoes a 10 minute rest period and a 6 minute walking test. Each of these two events are marked using the DR180 Holter apparatus.
Based on these data 29/30 waking activities ( 6 minute walks) were detected successfully and none of the 30 rest periods were detected. The only activity episode not to detected by the algorithm was the 6 minute walk from Patient 2, Baseline Evaluation. On closer inspection of the data, it was found that this patient was walking extremely slowly (40 steps per minute). However, the 6 minute walk from the same patient during her subsequent follow-ups were detected appropriately, because she walked faster than 40 steps per minute.
Another way of using the 24 hour data from heart failure patients is to compare the total ADL/per day from the heart failure patients with the normal subjects.
Figure 14 shows the daily activities (hours:minutes) for each of the 10 normal subjects and 30 data sets from heart failure patients. Mean ADL/day was 4 hours 51 minutes in 2o Normal subjects and is significantly greater than ADL/day of 2 hours and 10 minutes of the heart failure patients. Even though this result is not unexpected (one would expect the normal subjects to be more active than the heart failure patients) and the normal subjects are not age matched, it does provide a "reality check"
of the algorithm. In fact, one may argue that the ability to separate out normal subjects from heart failure subjects based on their activities is a desired attribute of an algorithm.
The general approach.
3o The algorithm accepts the filtered activity count for a two second interval. As mentioned previously, this is bandpassed data from a sensor such as an accelerometer that measures a count every time it moves. Various forms of activity sensor could be used which would require different pre processing. For example, if a three axis accelerometer were used, one could filter out or ignore the activity occurring outside of a particular plane of acceptable motion; such that a patient s moving up and down would register a count, but one moving sideways may not.
Alternatively only large excursions in signal output indicating a movement or shock of a sufficient size could be employed, thereby removing small movements form the calculation. If motion is detected from changes in a resonant microbeam sensor due to stress or pressure, different pre filtering schema must likewise be adopted. What is essential is that a mechanism be adopted to generate a count that gives a rough absolute value range for the amount of movement or change experienced by the sensor over a short period of time, and that this value then be averaged or otherwise compared to the other short period activity values collected over a larger period of time. In our preferred embodiment we used 2 seconds for the short period and one ~5 minute for the long period because of convenience, but any vaguely similar pair could be used, for examples, one second short over 2 minute long periods, or thirty second short over half hour long periods., One could even name the long periods to correspond to day times to produce a display color or three dimensions with values for the activity counts per hour displaying more toward the red side of a spectrum 2o for higher activity and more toward the blue for less, or vice versa, thus providing a . diurnal chart.
While all these variations and more will naturally occur to one of ordinary skill in this art, we have found it to be perfectly acceptable to use a single axis 25 accelerometer or piezocrystal with a value of 1.5 counts per minute as reasonable for cardiac patients. The amount of activity of a cardiac patient will generally be quite low on average and we want to pick up even small activities in the case of the CHF patient. Our studies indicated that an activity level of a 95 steps per minute over the minutes walked of a healthy person was about 23, but at 60 steps per 3o minute the average count was more like 5 or 8 counts per walked minute.
Since the CHF patient will be doing few walks of more than a minute the expectation is that nearly all two second periods will register a zero activity signal, which was born out through experiment. However, it should be recognized that if the small period chosen is larger, or the activity sensor more sensitive than what we used, a larger count may be expected, possibly necessitating an adjustment in the exclusion level above what we chose as 1.5 average counts per minute period. Similar adjustments to this value will occur to the reader without the need for undue experimentation.
So in general, in our preferred embodiment, to find the number of active 1 minute periods in a 24 hour period, a raw activity count is established for each two second Io period. To capture the patient in ADL, we use an average of 1.5 activity counts for each of the 2 second periods in a minute. Thus if the sum of activity counts in a minute, divided by the number of short periods is greater than 1.5, we declare the minute containing these short periods "active" . We then do this for a 24 hour period and add up the active periods to get our chartable data point. We could use is some other mathemetal function besides averaging, such as to pick the mean or maximum value, but these while possibly acceptable are less preferred. Many other well known mathematical functions could be used within the skill of the practitioner of this art to substitute for average if desired.
2o Therefore, in general, we are figuring on the basis of the number of short periods (like a 2 second interval), that a patient is determined to be active over the course of a first larger period, one can thus establish a clinically valuable data set of larger period activity that corresponds to the medically recognized functional status of the patient. In our preferred algorithms, we employed two period sizes larger than the 2s first larger period just mentioned to refine the available data and produce an easily useable display. The period larger than the first larger period is a day, in our preferred embodiments. Thus the number of active first larger periods in a day can easily be tallied and displayed as a percentage or number of hours in the day period.
In our most preferred embodiment, we average the value established each day over 3o a week, which allows for what we believe to be more useful information to be WO 99/58056 PC'f/US99/10282 displayed, since patient activity is normally quite variable over the course of a week and also so that a smaller display can be used for a large set of data.
Because this can be confusing, we use the following example for a detailed s discussion of the application of the concept for a 2-second count set over a minute period.
EXAMPLE 1.
cs = counts /2sec. period ~o If cs is an element of the set containing the count values for each 2 second period in a minute, i.e., {0,0,0,0,1,0,0,15,30,32,10,2,0,0,1,0,0,0,0,0,0,0,01,0,2,1,0,0,0}, then the average value (the sum of the elements' values divided by the number 30), then the value for that minute is approximately 2.7, which is greater than the 1.5 we 1s are using for the cut-off point, so a positive activity value gets recorded for this minute.
EXAMPLE 2.
if cs = counts/2 second period and If cs is an element of the set containing the count values for each 2 second 2o period in a minute, i.e., {0,0,0,0,0,0,1,0,0,0,1,0,0,1,5,3,2,0,0,0,0,2,8 ,0,0,0,0, ,3,0,1,0,0},then the average value (the sum of the elements' values divided by the number 30), for that minute is less than 1 so a negative or zero activity attribute is stored for that minute.
25 Depending on the kind of display desired, we can telemeter or otherwise communicate the data from an implanted device monitoring activity data in the way we describe which includes any subset of the data or all of it. Preferably a display of the amount of activity in a day, averaged over a week will be displayed for a number of months in a display similar to the graphs illustrated in Figs. 1-5.
3o Additionally, this data can be printed out for storage and later use by reference to a patient's file.
WO 99/58056 PG"fNS99/10282 Many adaptations can be made upon the information provided by this invention.
For one thing, a patient alarm may be sounded to incent the patient to comply with an exercise regimen or to call his doctor. Today small and low power piezo speakers with small speech producing circuitry are plentiful and inexpensive.
The data here if it shows a failure or inability of the patient to comply with an exercise regimen could actually speak to the patient and say things like, 'time to get on the treadmill' or 'time to go to the doctor' or something similar, based on the severity of the failure of the patient to achieve the activity goals set with his physician and to programmed into the device. Additional patient signals like a buzzer, shaker, or even electric shock could be provided to get the patient's attention. As already mentioned, if a particular activity pattern is developing, and this device is included in a drug pump, the drugs the patient is receiving could be adjusted based on his activity level, the angle of its downward or upward slope, or other characteristics is determinable based on this activity data set.
Also, if the device records arrhythmias sensed through auto triggering mechanisms or through patient activation of the event record we can report this data out with an indication of it's temporal correspondence to activity level. This can tell the 2o physician whether the arrhytmia occurred during rest or activity.
Arrhythmia monitoring background art, hereby incorporated by this reference includes US
patents numbered 5,113,869; 5,313,953; and 5,086,772. Also incorporated by reference are the following patents on triggering recordings of arrhythmic events, both automatically and by patient activation; 4,086,916; 5,404,877 and 5,012,814.
Another less preferred embodiment would be to use an external device strapped or otherwise affixed to a patient's body to collect the activity data, (requiring of course some adjusment to the prederemined value for deciding whether a minute period qualified to be called active), but considering the difficulty in gaining paitient 3o compliance or comfort, we feel the implantable versions will be most effications.
Many variations on the teachings of this invention may fall within its ambit, but the invention is only limited by the following claims.
Impairment of functional status can be seen in changes in the ability to perform exercises and ADL. This can be affected by many physiological factors such as ~s progressive decompensation in the setting of left ventricular cardiac (LV) dysfunction, beta blocker treatment, symptomatic arrhythmias, and depression.
These changes may take place over a long period of time and may be too subtle to be discerned by patients.
2o Physicians use answers to these questions and observation in clinic to determine what New York Heart Association "class" into which a patient falls, and on this basis, among others, they administer and alter treatment. Class I is defined as "Patients with cardiac disease but without resulting limitation of physical activity.
Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea or 25 anginal pain. " NYHA Class II is "Patients with cardiac disease resulting in slight limitation of physical activity. They are comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea or anginal pain. ", Class III is defined: "Patients with marked limitation of physical activity. They are comfortable at rest. Less than ordinary activity causes fatigue, palpitation, dyspnea, 30 or anginal pain. And, Class IV is "Patients with cardiac disease resulting in inability to carry on any physical activity without discomfort. Symptoms of heart failure or of the anginal syndrome may be present even at rest. If any physical activity is undertaken, discomfort is increased. "
As implantable device technology advances, there is a further need to provide 5 information that will allow the clinician to not only manage arrhythmias better but also the progression of other diseases (co-morbidities) that patients may have. With the advent of newer drugs and newer paradigms in drug therapy (the use of beta blockers in heart failure patients is just one example), there is a need for objective measures of patient response. Several parameters such as ventricular pressure, patient activity, lung wetness, and heart rate variability may provide such information to the clinician.
In the management of patient care over a relatively long period of time, it is believed that current implantable devices with their larger memories and even using ~s some extant sensors may be enhanced to produce a set of data that indicates patient functional status on an on-going basis. For patients who do not have already a need for an implanted medical device as an adjunct to their medical therapy, the addition of a specialized implantable that has extremely limited capability and thus small size may provide an additional tool for medical management of disease, particularly 2o Cardiac Heart Failure (CHF).
However, it seems that the simplest and possibly most accurate measurement which can determine the prognosis and progress of a patient has not been previously monitored, and further this indicator has not been monitored in a manner effective 2s to elucidate for the physician the changing character of the patient's CHF
disease progression.
If there were a simple and yet effective measure that could be reliably correlated with the progress of CHF, the use of other hemodynamic measures could be used to 3o supplement it and could easily be added to an implantable device. This indication alone may provide a sufficient justification for implantation of a device. In other words, if a very inexpensive implantable device could be developed to chronically monitor a simple indicator of CHF prognosis, the care available to CHF
patients could be improved by using this data. Administration of patient care based on this inexpensive implant's data would improve the lives of CHF patients by virtue of their needing less frequent clinic visits for drug titration and other observationally intense activities, since the status of the patient could be determined without resort to an expensive doctor visit by merely and viewing the data from these CHF
status indicators, the drugs themselves could be adjusted, alarms could be sent, and other therapies automatically adjusted based on this status report. It is believed that the ~o common usage of such systems awaits the development of an information resource such as is taught in this patent.
We have determined that a long term trend of physical activity in CHF patients may thus provide the clinician with an objective measure of the patient's life-style and 1s functional status, and can be used in conjunction with other information, but as explained previously, an objective long term measure is currently unavailable from implantable medical devices. Having available a display of a trend of patient ability to perform ADL is useful in several situations.
2o Correlation of physical activity with patient testimony.
Clinicians often encounter patients who find it difficult to verbalize their symptoms clearly. In such situations, an objective measure of patient activity stored in the device may help the clinician to decide an appropriate course of action. For 25 example, if a patient complains vaguely of fatigue and shortness of breath and is not able to describe the limitations to his/her daily activities, a trend of activities may help the clinician. If the activity data could show a considerable decrease in patient activities, then the clinician may take the next step such as the evaluation of cardiac profile, pulmonary dysfunction, or existing drug therapy. On the other hand, if the 30 long term trend of activity in this patient is consistently regular, (i.e., no decrease in patient activity), the clinician may take alternative steps to understand the difference between patient symptoms and device indicated activity data.
Clinicians may also encounter situations when patients are reluctant to discuss their s symptoms. In situations such as these, a trend of activity data may help the clinician to question the patient or the patient's spouse and enable the patient to come forward with their symptoms Correlation of physical activity with onset or progression of Heart Failure.
io Heart Failure is a syndrome characterized by the coexistence of left ventricular dysfunction (low EF), arrhythmias, pulmonary and peripheral congestion, and symptoms of fatigue and shortness breath. A majority of ICD patients have low EF
( < 40~) and decreased functional capacity (NYHA Class II, III and IV), and are at risk of developing heart failure. Clinical heart failure is a progressive disease;
is hence early identification and timely therapy may prevent hospitalizations, reduce the cost of care and improve patient lives.
In the earlier stages of heart failure, patients may not be able to perform strenuous activities and in the later stages, may not be able to perform even routine activities 2o such as walking up a flight of stairs. Further, the inability to perform exercise and activities develop over a long period of time and hence may be difficult to discern and quantify. An objective measure of long term trends of patient activity may be useful in early identification of symptoms of heart failure and in the progression of heart failure. A gradual decrease in patient activities over the last 8 months may 2s lead the clinician to suspect the development of heart failure. This may lead the clinician to take the next step in differential diagnosis.
The ability to perform daily activities is of particular relevance to the onset and progression of heart failure. Several studies in the literature have described the need 3o for an objective measurement of activities of daily living in patients with heart failure. This is no doubt why the NYHA measures focus so much on this ability to perform daily activities.
Correlation of physical activity with patient response to therapy.
s The use of beta-blockers in patients at risk of arrhythmias or heart failure is becoming common clinical practice. Beta-blocking agents are known to blunt the heart rate response, which may affect the patient's ability to perform certain activities. The optimal dosage of beta-blocking agents is difficult to predict and may require trial and error methods. Further, there is usually a 30-60 day time period 1o immediately after initiation of drug therapy during which the patient may not be very active. Most patients acclimate to the therapy after this period, but some don't.
By having an objective measurement of patient activities, the titration and adjustment to beta blockers and other drugs could be enhanced.
Correlation of physical activity with arrhythmias.
~s Arrhythmias may cause symptoms such as palpitations, fatigue, or presyncope.
Some patients may spend a significant amount of time in arrhythmias such as atrial fibrillation and may not be able to perform daily activities. Such issues can be coordinated with measurements of activity for increased diagnostic value.
As is known in the art, implantable medical devices exist that have various accelerometers and piezocrystal activity sensors and the like which count the movement of the crystal or sensor with respect to a resting state. Medtronic brand implantable medical devices with piezoelectric crystal or accelerometer based activity sensors have the ability to convert a raw activity signal into the 2 second activity counts. In other words, the number of times the accelerometer or sensor moves in a two second period is called a 2 second activity count. However, nothing in the art describes a method or apparatus for succinctly and effectively compiling such data as activity counts to make that data effective to solve the problems in diagnosis and patient tracking described above.
What is needed is a device with a system to convert these activity counts or some equivalent of them into a measure of patient activity that is clinically meaningful.
To review the specifics of the problem consider the raw signal. The raw activity signal and hence the processed activity counts is a result of vibrations due to body movement. Activities such as walking and running cause body movement and 1o vibration; the faster or longer the walk, more the vibrations, and larger the activity signal. Even though the raw activity signal is a good measure of activities such as walking and running, these raw counts, without our invention do not provide an accurate assessment of patient physical exertion. This is for three classes of reasons.
1. Body vibrations are not always proportional to level of exertion.
Any activity that causes body movement such as walking and running will generate an activity signal. Studies have shown that the amplitude of the activity 2o counts increases in a linear fashion for walking activities. However, this linear relationship between intensity of activity and the activity signal during walking does not apply to all activities. For example, walking up-stairs at the same speed will produce activity counts similar to walking on a flat surface even though the intensity of activity is higher while walking upstairs. Other examples are isometric exercise, stationary bike, and jogging in place. Since the activity counts are derived from this activity signal, they may not be an objective measure of patients' exertion levels for all types of activity. In fact even a simple activity such as walking may produce different activity counts depending on the human-ground interface, for example, walking on a carpeted surface versus an asphalt surface.
2. Lack of specificity.
Activities such as automobile driving that result in body vibrations but do not involve exertion may sometimes produce an activity signal that may be comparable in amplitude to the level of the activity signal during walking. Likewise, the s orientation of an accelerometer may not pick up an activity like push ups, despite the large exertion.
3. Inter-person variability.
1o There is also inter-person variability for similar activities. Differing size and fat content of the patient body, as well as placement of the device in various locations and orientations will all contribute to this kind of variability.
Based on these considerations, it is clear that in order to convert the activity counts to a meaningful measure of patient activity, the system must 1. be sensitive enough to pick up low-level activities (activities of daily living) as well as high-level exertion activities, 2. minimize response to activities such as automobile driving, 3. be patient independent, i.e., it should not require a user programmable parameter and should 4. be easy to implement in an implantable device Brief Description of the Drawings.
Figs. 1-3 are graphs of time versus activity counts over time in accord with preferred embodiments of the invention, used to describe various changes in patient 3o condition over time.
Figs. 4a-c are similar graphs of activity versus time.
Figs. Sa and Sb are graphs of atrial fibrillation versus time and activity versus time using the same time axis.
Fig. 6 is a graph indicating a NYHA class level with patient activity over time.
Fig. 7 is a drawing of the inside of a patient's body having three implants within it on the right side of a line BL and an external programmer for communication with 1o the implantable devices.
Summary of the invention.
This invention provides a patient activity monitor for chronic implant which is provides well correlated evidence of the functional status of the patient with the implant. It does so by monitoring an activity signal related to the movement of the patient, and from this monitored movement determining a level of activity indication signal value.
2o Detailed description of the preferred embodiments Nearly any currently implanted medical devices could be adapted to employ the features of this invention provided only that such a device maintains either a direct and constant link with a memory device or has its own memory device and its own activity sensor, and that there is provided an appropriate processing circuit or 25 program to enable the invention activity. Activity sensors are well known and have been employed in pacemaker type implantable medical devices for many years. A
typical such device is seen in Strandberg's US patent No. 4,886,064, and it is now common to see the basic activity sensor combined with alternative means for sensing activity such as minute ventilation as in US Patent No. 5,562,711, both of 3o which are hereby incorporated by this reference in their entireties.
Referring now to Fig. 7, in which a set of alternative implantable devices 71a-c are illustrated inside of a patient body, (the edge of which is here illustrated as line BL,) the typical application of this invention will be to provide data for a display 82 on an external device such as a programmer P external to the patient body, via a conununications channel such as is here represented by double headed arrow C.
The data may be shown in the form of a bar chart or line graph or similar display which indicates the total amount of some algorithmically derived measure of activity over a given period of time, such as a.day or an hour. Device 71a is a pacemaker having a memory 75a which stores the data measured by the sensor 76a. The storage can be in a raw form if there is sufficient memory or it can be compressed in various advantageous forms by a program or other circuit device running a process such as processor 77a. In this embodiment, the microprocessor 77a runs a program stored in memory to convert sensed activity counts processed through an analog to digital converter 79 as they appear on a bus 78, and then returns the 1s processed data to the program for temporary storage in the memory circuit 75a.
When enough measurements are made in accord with the program, the microprocessor converts a representation of the total to a value and stores the representation in the memory. When an external device such as a programmer P
requests a dump of these stored representations of value indicating the amount of 2o patient activity over time, it is formatted and sent over the communications channel by a communications circuit 83 to the external device so that it can be displayed in a human readable form. Alternatively, of course, the data can be sent via communications here simply represented by arrow C2 and arrow L1 to be stored in a temporary device TD for later relaying to other devices by phone or othe 25 rtelemetry, here represented simply by line L2, for later or contemporaneous but distant display. Only the simplest construction required for operative use of the invention is shown here. It is a simple matter to resend data over modern communications systems once it is retrieved in a machine readable format. As most implantable cardioverter defibrillators and pacemakers of today have 3o microprocessors, memories and activity sensors, the addition of this invention to such devices would require no additional hardware, but mere software reconfiguration to accommodate the requirements of storing appropriate activity data in a useful manner so as a to be available for use in accord with this invention.
Also, alternative forms of implant devices can be used. The Medtronic device REVEAL(TM), for example is fully implantable and similar to the device illustrated as device 71 c, with a memory and processing circuitry for storing electrocardiograms, taken across two electrodes 79 and 72c. The addition of an additional circuit for sensing activity and appropriate circuitry to implement the storage of the relevant activity data in an appropriate manner would make this type of device another good candidate for the inventive features herein described.
Also a drug pump such as device 71b when outfitted with the appropriate memory, processing and sensing circuits could do the same, as could other currently implantable devices. It should be noted that a device with nothing but an activity sensor, memory, a processor and some form of communications circuit would also be sufficient to perform the tasks required of this invention's implantable device.
By using a kinetic power source, the dependence on a battery could be eliminated too and the device could be extremely small and unobtrusive, permitting the clinician to easily obtain patient consent to accept the implant.
2o Once the valuable functional status data is available, any communications system could be employed to get the information to the doctor, or to update the patient file.
Any such uses of the information provided by this invention are contemplated.
Fig. 1 is a prototype illustration (using simulated data) over a 14 month period of 2s patient activity data. Note that the patient can be seen over a very long period and any changes in the activity amount will be readily visible. Also, as illustrated here, even fairly large variations in daily activity will not influence the overall impression of health. Here the graph 10 displays hours in which the activity count was in the active range per day 12 versus time 13 on the line 11. In Fig. 2, the activity line 15 3o begins trending downward in late September confirming that the patient should be seen at the time shown 16.
Figure 3 illustrates a long term trend of patient activity data. This situation may be that of a post myocardial infarction (MI), sudden cardiac death survivor with EF <
40 % .
In the Figs. 4a-c are prototypical illustrated activity trends for patients with a drug therapy intervention. In case 1, Fig 4a, graph 20a, line 21a shown little or no effect of the drug regimen. the patient report at the time he is seen will probably indicate no change in status, based on the height of curve 21a. In case 20b, the to drug administration has an apparent effect by the time the patient is seen, which in graph 20c, the line 21c shown no effect of the Beta blocker administration.
Thus, objective measurement of patient activities as illustrated in Figures 4a-c can provide feedback to the clinician during the use of beta blocker therapy. Figure 4a is a scenario where activities are not decreased. Fig. 4b illustrates a temporary decrease ~5 in patient activities followed by an increase to the original level. Fig.
4c shows that patient activities decreased and stay decreased, and may require the modification to the drug dosage.
Figs 5 a and Sb show how the activity data can be coordinated with a display of 20 other patient related data, here hours in atrial fibrillation per day. Note the apparent correlation with the height of the line 21d and the increased occurrence of Atrial Fibrillation as would be expected. A device such as 71c which could track both fibrillation's and activity could be used to produce such a paired graph display. The ability to correlate the duration of arrhythmia episodes with patient activity as 2s shown in Figure 5 may help the physician in treating the arrhythmias. For example, if the patient is being monitored with a device that can display the occurrence of an arrhythmia or an accumulation of arrhythmic events over time as shown in this figure, or if such a feature is incorporated into the inventive device, the physician can learn immediately whether the arrhythmia occurs with activity, an 3o important piece of diagnostic information.
Figure 6 illustrates correspondence with physician determined NYHA Class level of a CHF patient over a relatively short time period with his activity level in hours along the vertical axis. This chart shows the NYHA Classification and activities per day for the 7 follow-ups. As can be seen in the figure, activities per day appear to 5 be increasing over time in correlation with the improvement in NYHA
functional Classification from Class III to Class I/II.
In order to explain how the compilation of useful information from the raw data can be accomplished, refer first to Fig. 8 in which activity counts from a normal subject collected over a 24 hour period is shown.
These signals can be collected from pacemakers which have been using piezoelectric crystals and accelerometers mounted inside the pacemaker can as an indicator of patient activity to control the rate of the pacemaker. Typically, the raw 1s acceierometer/crystal signal is first filtered using a bandpass filter and the total number of crossings above and below a fixed threshold is calculated every 2 seconds. The rate of the pacemaker is then calculated based on these 2 second activity counts. These 2 second activity counts can also be used to acquire information regarding patient activity. For heart rate and Heart Rate 2o Variability(HRV), most pacemaker devices already have the ability to calculate heartbeat intervals and changes in these calculated variables over time gives the HRV. Other measures such as breathing rate, oxygen saturation, blood pressure, temperature, or just about any other measurement that can be made could be coordinated with the activity display to provide useful information, but we only show HRV here to teach that such can be easily done, not to limit the invention to this one coordination display.
These observations are evidence that the concept of using the accelerometer signal to differentiate between activities and non-activities is appropriate and acceptable.
Description of candidate algorithms for implementing this- invention~
Based on the concerns just described we selected 5 candidate algorithms for determining a value for activity level per day, and we tested these on a large set of 24 hour data. A description of the 5 algorithms follow. These are examples only.
We ultimately selected as a most preferred algorithm one which uses 60second periods rather than 80, to provide easier conversion to minutes and hours. We also found that in producing a display it was sensible to average the daily values for a week, since normal activity cycles over a week can vary significantly over a weekend, particularly.
ADLl: First, the average activity count over 80seconds is calculated by adding consecutive 2 second counts. The average is then compared with a threshold of 1.5.
If the average is greater than or equal to 1.5, then the 80s period is considered as activity (ADL), and the average for the next 80s time period is calculated. At the end of the 24 hour data, total activity duration is calculated by adding the number of 80 second windows that were detected as ADL, and multiplying with 80.
ADL2: This algorithm is similar to ADL1 except for the choice of threshold (2.0).
This choice was made to evaluate the trade-off between detecting true ADL and 2o driving, a lower threshold will detect most ADL as well as driving while a higher threshold will detect lesser driving and true ADL.
ADL3: This algorithm is similar to ADL2 except for the choice of window. Since we do not have any a priori knowledge of the typical duration of activities of daily living, a choice of 40 seconds was made in order to study the difference in 25 performance between the 80 second and the 40 second windows.
ADL4: As described above, ADLl, ADL2 and ADL3 are threshold algorithms and do not attempt to separate out non-activities from activities based on variability (only amplitude information is used). In the ADL4 algorithm, the 80 second average is first calculated. The number of 2 second counts that are greater than 0 3o are also noted. If the 80 second average is greater than 1.0 and at least second counts in the 80 second window are greater than 0, then the 80 second window is detected as ADL.
ADLS: This algorithm is another way to use the variability information. The 80 second window is separated into 20 second sub-windows and the average for the s second window as well as the 4 sub-windows are calculated. If the average of the 80 second window is greater than 1.0 and 3/4 sub-window averages are greater than 0.3 *(average of 80 second window), then the 80 second window is detected as ADL.
t o Algorithm Evaluation The 24 hour data set we used was collected from 10 normal subjects and separated into two groups, a Development data set and a Validation data set. Three performance measures P 1, P2 and P3 were calculated for each data set in the ~5 Development data set. Algorithm parameters were adjusted to achieve the highest P1 and P2, and lowest P3. Since these performance measures are different from the traditional sensitivity and specificity measures used to evaluate algorithms, each of these measures is defined and explained below.
Performance measure P1 PI =
Duration of marked ADL that is detected by al orithm) Duration of marked ADL as marked by subject (gold standard) We had the normal subjects mark those times they felt they were active. Upon close inspection of the marked ADL, it was found that several marked ADL
events were of the stop-go-stop-go type, i.e., there were periods of rest in between short bouts of activity, which is characteristic of activities of daily living. In order not to 3o penalize the algorithm for the detection of rest periods in between marked ADLs, only that part of marked ADL that was associated with an elevated heart rate ( heart rate during rest period during day + 10 bpm) was considered as marked ADL.
This procedure can be thought of as refining the gold standard data.
s Performance measure P2 This measure was used to ascertain whether the algorithm was detecting non-activities as ADL. Since not all activities and non-activities are marked, a heart rate based criterion was used to differentiate between activities and non-activities.
Specifically, activities that were associated with a elevated heart rate (resting heart rate during day + 10 bpm).
P2 =
Total time detected as ADL associated with a elevated heart rate (P2N) is Total time detected as ADL (P2D) For example, to calculate P2 from a 24 hour data set, P2D is calculated as the # of 80 second activity windows detected as ADL by the algorithm. To calculate P2N, 2o the heart rate in the 80 second window corresponding to the 80 second activity window that is detected as an ADL is calculated first. If the heart rate in this window is above (rest heart rate + 10 bpm), then the 80 second activity window is said to be appropriately detected. The ratio of P2N and P2D is the performance measure P2.
2s Performance measure P3 This measure is used to ascertain the ability of the algorithm to eliminate detection of automobile driving as ADL. Automobile driving (or riding in a bumpy 3o conveyance of any type) we believe will be one of the main causes of false positives. This measure P3 is calculated as the ratio of P3N and P3D, where P3D is the total duration of marked driving by the normal subject and P3N is the duration during which marked driving was detected as ADL. Clearly, the lower P3 is, the better the algorithm.
P3 =
_ Total driving time that is detected as ADL (P3N) Total marked driving time (P3D) Calculation of heart rate To obtain heart rate data, 24 hour surface ECG data stored in a Holter Monitor system were downloaded and analyzed using "Hotter for Windows"(TM) software (available through Rozin Electronics, Glendale NY). Every beat of the ECG data (each beat corresponds to the detection of a QRS complex) was classified as a Normal beat, Ventricular beat, Supra-ventricular beat or an Artifact. Only intervals ~5 between normal (sinus) beats (referred to as NN intervals) were used to compute heart rates during the entire 24 hour period.
The accelerometer signal is processed and a raw count is calculated every 2 seconds (ACTCNT) for pacemaker rate response. An algorithm to calculate minutes of ADL
2o from ACTCNT is as follows:
[Where NUM is the total number of activity counts, THRESH is the threshold for whether the activity count value will be counted as a one or a zero in the SUM
of counts, and DAILYCOUNT is the variable value to be displayed for a single day.
It should be noted that if the values of a week of DAILYCOUNTs are averaged to 2s provide a single point to display as was the case for the graphs of Figs. 1-5, the average value will be what is displayed, but for more detailed analysis, even if averaging is used, the DAILYCOUNT values would preferably be retained beyond a given week, unless the device in question is operating with minimal memory capacity.
Step i . Starting at 12 a. m. (00:00:00), add NUM ACTCNT, i.e., SUM = S ACTCNT/ NUM.
s Step 2. If SUM > THRESH, increment a counter (DAILYCOUNT) else next step.
Step 3. Repeat Step 1 and 2 continuously till the next 12 midnight Step 4. Save DAILYCOUNT for this day and repeat Steps 1-4 to If averaging over a larger period, average the DAILY COUNT values for the past number of days in the period and establish a larger value representing the average, AVDAILYCOUNT for the period, so that that value AVDAILYCOUNT can be displayed.
NUM and THRESH are 1 byte programmable Recommended values are NUM = 30, THRESH = 45 2o Memory requirements:
For NUM = 30, DAILYCOUNT could have a maximum value of 1440 and would require 2 bytes of storage every day.
Total memory: 2 bytes a day (850 bytes for 425 days) Software algorithm and printing Step 1. ADLDAY = DAILYCOUNT * NUM * 2 / 60 3o Step 2. ADL= Avg. ( 7 consecutive ADLDAY ) Graph ADL every week as illustrated in "Long Term Clinical Trends "
Y axis title: "Total hours of patient activity per day"
Y axis tick labels: " 0 2 4 > 6 " Hours s Algorithm Evaluation As detailed above, each of the 5 candidate algorithms were applied to each of the 5 data sets in the Development data set and the three performance measures Pl, and P3 calculated. The algorithm parameters were changed to maximize P1 and P2 1 o and minimize P3. The values and brief descriptions of Pl , P2 and P3 for each of these 5 normals is shown in Table 2. Following algorithm development, the algorithms were applied to the Validation data set and the process repeated.
Table 3 shows the performance measures for the Validation data set. It is important to note that the utility of the performance measures is limited to the comparison of the 1s different algorithms only and is not meant to be used as an absolute measure of sensitivity and specificity.
As detailed in the Methods section, each of the 5 candidate algorithms were applied to each of the 5 data sets in the Development data set and the three performance 2o measures P 1, P2 and P3 calculated. The algorithm parameters were changed to maximize P1 and P2 and minimize P3. The values and brief descriptions of P1, and P3 for each of these 5 normals is shown in Table 2. Following algorithm development, the algorithms were applied to the Validation data set and the process repeated. Table 3 shows the performance measures for the Validation data set.
It is 2s important to note that the utility of the performance measures is limited to the comparison of the different algorithms only and is not meant to be used as an absolute measure of sensitivity and specificity.
pl ~ P3 ADL1 70 % 86 % 28 %
ADL2 62 % 91 % 20 ADL3 60 % 89 % - 20 ADL4 72 % 86 % 42 %
ADLS 65 % 87 % 36 Pl: Ability of the algorithm to detect marked ADL activities P2: Ability of the algorithm to detect activities that are associated with an increase in heart rate P3: Ability of the algorithm to detect automobile driving.
Table 2. Performance measures for Development data set pl pZ P3 ADLl 72 % 80 % 24 %
ADL2 66 % 85 % 14 %
ADL3 63 % 84 % 14 ADL4 74% $0% 36%
ADLS 66 % g0 % 28 1o Table 3. Performance measures for Validation data set Several observations can be made from these results. Given that the goal of the algorithm is to maximizx P1 and P2 and minimize P3, it is clear from Table 3 that algorithm ADL1 and ADL2 best meet the criteria. The only difference between ADL1 and ADL2 is the threshold parameter, ADL1 had a threshold of 1.5 and s ADL2 had a threshold of 2Ø It is to be expected that a higher threshold (ADL2) would lead to a decreased sensitivity (P1 is lower for ADL2) and but increased specificity (P2 is higher for ADL2). The only difference between ADL2 and ADL3 is the choice of window (ADL2 used a 80s window while ADL3 used a 40s window). Based on the results, it appears that decreasing the window from 80s to 40s had a minimal effect on the performance measures. This may be because most ADL may have been longer than 40 seconds. The variability algorithms do not appear to detect driving any less than the threshold type algorithms. This may be because of the fact that the variability of driving while evident in the 2 second data may not be a factor when data is averaged over 80 seconds. Table 3 shows the ~s . ~ performance for the Validation data set. The results from the Validation data set are consistent with the observations made from the Development data set.
Based on these observations, ADL1 (essentially same as ADL2) algorithm was chosen as the algorithm of choice since this algorithm has the highest sensitivity for 2o marked ADL activities. Coincidentally, this algorithm is associated with the least implementation complexity.
Comparison of ADL between normals and heart failure patients As explained in the Data Collection section, 24 hour activity and heart rate data 2s were collected from 10 heart failure patients as part of the EXACT study in addition to the 10 normal subjects as part of the Normal study. Such data were collected 7 times from each patient over a period of 16 weeks. However, detailed diary data were not collected from the patients as was from the normal subjects. Even though the 24 hour EXACT data from the patients do not lend themselves to the type of analysis performed on the data from normal subjects, these data were used to study algorithm performance in two different ways.
Thirty (30) data sets, each consisting of 24 hour heart rate and activity data from a s patient were analyzed using algorithm ADL1. At the beginning of the 24 hour follow-up, each patient undergoes a 10 minute rest period and a 6 minute walking test. Each of these two events are marked using the DR180 Holter apparatus.
Based on these data 29/30 waking activities ( 6 minute walks) were detected successfully and none of the 30 rest periods were detected. The only activity episode not to detected by the algorithm was the 6 minute walk from Patient 2, Baseline Evaluation. On closer inspection of the data, it was found that this patient was walking extremely slowly (40 steps per minute). However, the 6 minute walk from the same patient during her subsequent follow-ups were detected appropriately, because she walked faster than 40 steps per minute.
Another way of using the 24 hour data from heart failure patients is to compare the total ADL/per day from the heart failure patients with the normal subjects.
Figure 14 shows the daily activities (hours:minutes) for each of the 10 normal subjects and 30 data sets from heart failure patients. Mean ADL/day was 4 hours 51 minutes in 2o Normal subjects and is significantly greater than ADL/day of 2 hours and 10 minutes of the heart failure patients. Even though this result is not unexpected (one would expect the normal subjects to be more active than the heart failure patients) and the normal subjects are not age matched, it does provide a "reality check"
of the algorithm. In fact, one may argue that the ability to separate out normal subjects from heart failure subjects based on their activities is a desired attribute of an algorithm.
The general approach.
3o The algorithm accepts the filtered activity count for a two second interval. As mentioned previously, this is bandpassed data from a sensor such as an accelerometer that measures a count every time it moves. Various forms of activity sensor could be used which would require different pre processing. For example, if a three axis accelerometer were used, one could filter out or ignore the activity occurring outside of a particular plane of acceptable motion; such that a patient s moving up and down would register a count, but one moving sideways may not.
Alternatively only large excursions in signal output indicating a movement or shock of a sufficient size could be employed, thereby removing small movements form the calculation. If motion is detected from changes in a resonant microbeam sensor due to stress or pressure, different pre filtering schema must likewise be adopted. What is essential is that a mechanism be adopted to generate a count that gives a rough absolute value range for the amount of movement or change experienced by the sensor over a short period of time, and that this value then be averaged or otherwise compared to the other short period activity values collected over a larger period of time. In our preferred embodiment we used 2 seconds for the short period and one ~5 minute for the long period because of convenience, but any vaguely similar pair could be used, for examples, one second short over 2 minute long periods, or thirty second short over half hour long periods., One could even name the long periods to correspond to day times to produce a display color or three dimensions with values for the activity counts per hour displaying more toward the red side of a spectrum 2o for higher activity and more toward the blue for less, or vice versa, thus providing a . diurnal chart.
While all these variations and more will naturally occur to one of ordinary skill in this art, we have found it to be perfectly acceptable to use a single axis 25 accelerometer or piezocrystal with a value of 1.5 counts per minute as reasonable for cardiac patients. The amount of activity of a cardiac patient will generally be quite low on average and we want to pick up even small activities in the case of the CHF patient. Our studies indicated that an activity level of a 95 steps per minute over the minutes walked of a healthy person was about 23, but at 60 steps per 3o minute the average count was more like 5 or 8 counts per walked minute.
Since the CHF patient will be doing few walks of more than a minute the expectation is that nearly all two second periods will register a zero activity signal, which was born out through experiment. However, it should be recognized that if the small period chosen is larger, or the activity sensor more sensitive than what we used, a larger count may be expected, possibly necessitating an adjustment in the exclusion level above what we chose as 1.5 average counts per minute period. Similar adjustments to this value will occur to the reader without the need for undue experimentation.
So in general, in our preferred embodiment, to find the number of active 1 minute periods in a 24 hour period, a raw activity count is established for each two second Io period. To capture the patient in ADL, we use an average of 1.5 activity counts for each of the 2 second periods in a minute. Thus if the sum of activity counts in a minute, divided by the number of short periods is greater than 1.5, we declare the minute containing these short periods "active" . We then do this for a 24 hour period and add up the active periods to get our chartable data point. We could use is some other mathemetal function besides averaging, such as to pick the mean or maximum value, but these while possibly acceptable are less preferred. Many other well known mathematical functions could be used within the skill of the practitioner of this art to substitute for average if desired.
2o Therefore, in general, we are figuring on the basis of the number of short periods (like a 2 second interval), that a patient is determined to be active over the course of a first larger period, one can thus establish a clinically valuable data set of larger period activity that corresponds to the medically recognized functional status of the patient. In our preferred algorithms, we employed two period sizes larger than the 2s first larger period just mentioned to refine the available data and produce an easily useable display. The period larger than the first larger period is a day, in our preferred embodiments. Thus the number of active first larger periods in a day can easily be tallied and displayed as a percentage or number of hours in the day period.
In our most preferred embodiment, we average the value established each day over 3o a week, which allows for what we believe to be more useful information to be WO 99/58056 PC'f/US99/10282 displayed, since patient activity is normally quite variable over the course of a week and also so that a smaller display can be used for a large set of data.
Because this can be confusing, we use the following example for a detailed s discussion of the application of the concept for a 2-second count set over a minute period.
EXAMPLE 1.
cs = counts /2sec. period ~o If cs is an element of the set containing the count values for each 2 second period in a minute, i.e., {0,0,0,0,1,0,0,15,30,32,10,2,0,0,1,0,0,0,0,0,0,0,01,0,2,1,0,0,0}, then the average value (the sum of the elements' values divided by the number 30), then the value for that minute is approximately 2.7, which is greater than the 1.5 we 1s are using for the cut-off point, so a positive activity value gets recorded for this minute.
EXAMPLE 2.
if cs = counts/2 second period and If cs is an element of the set containing the count values for each 2 second 2o period in a minute, i.e., {0,0,0,0,0,0,1,0,0,0,1,0,0,1,5,3,2,0,0,0,0,2,8 ,0,0,0,0, ,3,0,1,0,0},then the average value (the sum of the elements' values divided by the number 30), for that minute is less than 1 so a negative or zero activity attribute is stored for that minute.
25 Depending on the kind of display desired, we can telemeter or otherwise communicate the data from an implanted device monitoring activity data in the way we describe which includes any subset of the data or all of it. Preferably a display of the amount of activity in a day, averaged over a week will be displayed for a number of months in a display similar to the graphs illustrated in Figs. 1-5.
3o Additionally, this data can be printed out for storage and later use by reference to a patient's file.
WO 99/58056 PG"fNS99/10282 Many adaptations can be made upon the information provided by this invention.
For one thing, a patient alarm may be sounded to incent the patient to comply with an exercise regimen or to call his doctor. Today small and low power piezo speakers with small speech producing circuitry are plentiful and inexpensive.
The data here if it shows a failure or inability of the patient to comply with an exercise regimen could actually speak to the patient and say things like, 'time to get on the treadmill' or 'time to go to the doctor' or something similar, based on the severity of the failure of the patient to achieve the activity goals set with his physician and to programmed into the device. Additional patient signals like a buzzer, shaker, or even electric shock could be provided to get the patient's attention. As already mentioned, if a particular activity pattern is developing, and this device is included in a drug pump, the drugs the patient is receiving could be adjusted based on his activity level, the angle of its downward or upward slope, or other characteristics is determinable based on this activity data set.
Also, if the device records arrhythmias sensed through auto triggering mechanisms or through patient activation of the event record we can report this data out with an indication of it's temporal correspondence to activity level. This can tell the 2o physician whether the arrhytmia occurred during rest or activity.
Arrhythmia monitoring background art, hereby incorporated by this reference includes US
patents numbered 5,113,869; 5,313,953; and 5,086,772. Also incorporated by reference are the following patents on triggering recordings of arrhythmic events, both automatically and by patient activation; 4,086,916; 5,404,877 and 5,012,814.
Another less preferred embodiment would be to use an external device strapped or otherwise affixed to a patient's body to collect the activity data, (requiring of course some adjusment to the prederemined value for deciding whether a minute period qualified to be called active), but considering the difficulty in gaining paitient 3o compliance or comfort, we feel the implantable versions will be most effications.
Many variations on the teachings of this invention may fall within its ambit, but the invention is only limited by the following claims.
Claims (27)
1. An implantable medical device for tracking patient functional status comprising a housing having an activity sensor and circuitry therein for recording output from said activity sensor such that said output is compressed in accord with a processing circuit constructed to operate a procedure to iteratively, after the expiry of each of a first size time interval to:
add up via a summation means a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then to repeat this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then to determine via a computation means a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
add up via a summation means a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then to repeat this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then to determine via a computation means a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
2. A device as set forth in claim 1 further comprising means for providing said first display values to a display means for displaying a second display value wherein a sum of said first display values.
3. A device as set forth in claim 1 wherein said processing circuit is adapted to further iteratively determine if said first display value is a predetermined function of a predetermined alarm value, and for storing a result of said determination, then setting an alarm flag representing the result of said determination.
4. A device as set forth in claim 3 wherein said alarm value flag activates a patient alarm to notify the patient.
5. A device as set forth in claim 4 wherein said patient alarm is an audible sound generator.
6. A device as set forth in clam 5 wherein said audible sound generator is adapted to produce intelligible speech.
7. A device as set forth in claim 1 wherein said processing circuit is adapted to further determine if activity values represented by said first display values match a programmable pattern of activity level, and to set a flag indicating such a change.
8. A medical information display system for use with a device as set forth in claims 1 or 3-7 having means to receive data from said implantable device memory and processing means for generating a display, such that said display may display a representation of said first display values in a manner such that said first display correspond to the height on an axis parallel to patient status, and wherein said display has a perpendicular axis for displaying a progression of heights of said first display values for a first time unit varying over a time period comprised of multiple time units.
9. A device as set forth in claim 8 wherein said processing circuit of said display means is further adapted to average all said second values over an intermediate period of time such that said display of said first display values is represented by a single value for said intermediate time.
10. A device as set forth in claim 1 wherein said device housing further has additional sensor means for measuring values of additional physiologic parameters of a patient into which said device may be implanted and producing measurement output therefrom, and wherein said circuitry for processing and storing values processes and stores temporally related to said first first display values, additional physiologic measurement values taken from said additional sensor means output.
11. A device as set forth in claim 10 wherein said additional sensors sense physiologic signals including those drawn from the set of signals (arrhythmias, arrhythmias during rest periods, respiration rate during rest periods, respiration rate, occurrence of abnormal breathing episodes during rest, change in sinus rate during rest, heart rate variability, blood pressure, ST segment variation, blood oxygenation, temperature).
12. A medical system as set forth in claim 8 further comprising a receiving device for receiving patient data from said implant including representations of said first display values having a communications link to a communications system to enable the communication of said representation of said first display values to a remote device for subsequent display.
13. A method of tracking patient functional status comprising:
measuring activity counts with an implantable activity sensor, recording output from said activity sensor such that said output is compressed in accord with a procedure which iteratively, after the expiry of each of a first size time interval:
adds up a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then repeating this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then determining a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
measuring activity counts with an implantable activity sensor, recording output from said activity sensor such that said output is compressed in accord with a procedure which iteratively, after the expiry of each of a first size time interval:
adds up a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then repeating this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then determining a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
14. A method as set forth in claim 13 further comprising providing said first display values to a display means and then displaying a second display value comprising a representation of a sum of said first display values for a diurnal time period.
15. A method as set forth in claim 13 comprising the additional steps of further iteratively determining if said first display value is a predetermined function of a predetermined alarm value, and for storing a result of said determination, then setting an alarm flag representing the result of said determination.
16. A method as set forth in claim 13 wherein if said alarm value flag is set, activating a patient alarm to notify the patient.
17. A method for displaying representations of said first display values generated by any of the methods as set forth in claims 13 or 14-17 further comprising the step of receiving said first display values for generating a display, and adding up all the first display values for a diurnal period and displaying a visual representation of said value of said sum for each said diurnal period in a range of diurnal periods.
18. A method as set forth in claim 17 comprising the intermediate step of averaging all said diurnal period values for a week and then displaying a visual representation of the value of the week average for the week.
19. A method as set forth in claim 13 further comprising communicating the first display values to an external device.
20. A method as set forth in claim 19 further comprising activating an alarm system in said external device for setting a flag to take corrective action if the display values match a predetermined pattern.
21. A method as set forth in claim 17 further comprising titrating patient medication levels based on review of the display.
22. A device as set forth in claim 1 wherein said device housing further has additional sensor means for measuring values of additional physiologic parameters of a patient into which said device may be implanted and producing measurement output therefrom, and wherein said circuitry for processing and storing values processes and stores temporally related to said first first display values, additional physiologic measurement values taken from said additional sensor means output.
23. A medical system as set forth in claims 1-11 or 22 further comprising a receiving device for receiving patient data from said implant including representations of said first display values having a communications link to a communications system to enable the communication of said representation of said first display values to a remote device for subsequent display.
24. An implantable medical device comprising means for tracking activity counts over a small unit of time and for storing a compilation of values related to the combined sums of said activity counts for the sum of small units of time over a larger unit of time and having means for recording arrhythmic cardiac events such that data from said compilation of values is stored with an indication of temporal relatedness to any arrhythmic events which occur near in time to said compilation of values being a predetermined value.
25 An implantable medical device having an activity crystal for determining a counts value for each "a" second period, a processing circuit for averaging each said counts value over the number of a second counts in each minute, and having a memory circuit for storing a positive activity value for said average counts value for each minute if said average counts value is greater than a predetermined value.
26. An implantable medical device as set forth in claim 25 further comprising a processing circuit for storing the sum of all said positive activity values.
27. A device affixed to a patient for tracking patient functional status comprising a housing having an activity sensor and circuitry therein for recording output from said activity sensor such that said output is compressed in accord with a processing circuit constructed to operate a procedure to iteratively, after the expiry of each of a first size time interval to:
add up via a summation means a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then to repeat this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then to determine via a computation means a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
add up via a summation means a number of activity counts sensed per a first one of each first time intervals to produce a first sum, and if said first sum is greater than a predetermined value, storing a count value representing the result of comparing said first time interval first sum to said predetermined value in a memory means, then to repeat this add up procedure for subsequent ones of said first size intervals and storing a count value for said first size time intervals until a second size time interval is reached, then to determine via a computation means a first display value for a representation of said recorded first and second values, and storing said first display value representing said total value for each said second time interval.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/078,221 | 1998-05-13 | ||
US09/078,221 US6045513A (en) | 1998-05-13 | 1998-05-13 | Implantable medical device for tracking patient functional status |
PCT/US1999/010282 WO1999058056A1 (en) | 1998-05-13 | 1999-05-11 | Implantable medical device for tracking patient functional status |
Publications (1)
Publication Number | Publication Date |
---|---|
CA2331500A1 true CA2331500A1 (en) | 1999-11-18 |
Family
ID=22142698
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CA002331500A Abandoned CA2331500A1 (en) | 1998-05-13 | 1999-05-11 | Implantable medical device for tracking patient functional status |
Country Status (6)
Country | Link |
---|---|
US (3) | US6045513A (en) |
EP (1) | EP1079733B1 (en) |
JP (1) | JP2002514454A (en) |
CA (1) | CA2331500A1 (en) |
DE (1) | DE69941356D1 (en) |
WO (1) | WO1999058056A1 (en) |
Families Citing this family (294)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6129744A (en) * | 1997-12-04 | 2000-10-10 | Vitatron Medical, B.V. | Cardiac treatment system and method for sensing and responding to heart failure |
US6821249B2 (en) * | 1999-03-08 | 2004-11-23 | Board Of Regents, The University Of Texas | Temperature monitoring of congestive heart failure patients as an indicator of worsening condition |
US20070021979A1 (en) * | 1999-04-16 | 2007-01-25 | Cosentino Daniel L | Multiuser wellness parameter monitoring system |
US20060030890A1 (en) * | 1999-04-16 | 2006-02-09 | Cosentino Daniel L | System, method, and apparatus for automated interactive verification of an alert generated by a patient monitoring device |
US8419650B2 (en) | 1999-04-16 | 2013-04-16 | Cariocom, LLC | Downloadable datasets for a patient monitoring system |
US8438038B2 (en) * | 1999-04-16 | 2013-05-07 | Cardiocom, Llc | Weight loss or weight management system |
US7577475B2 (en) * | 1999-04-16 | 2009-08-18 | Cardiocom | System, method, and apparatus for combining information from an implanted device with information from a patient monitoring apparatus |
US7945451B2 (en) * | 1999-04-16 | 2011-05-17 | Cardiocom, Llc | Remote monitoring system for ambulatory patients |
US6290646B1 (en) | 1999-04-16 | 2001-09-18 | Cardiocom | Apparatus and method for monitoring and communicating wellness parameters of ambulatory patients |
US6294993B1 (en) * | 1999-07-06 | 2001-09-25 | Gregory A. Calaman | System for providing personal security via event detection |
CA2314517A1 (en) | 1999-07-26 | 2001-01-26 | Gust H. Bardy | System and method for determining a reference baseline of individual patient status for use in an automated collection and analysis patient care system |
US6493579B1 (en) * | 1999-08-20 | 2002-12-10 | Cardiac Pacemakers, Inc. | System and method for detection enhancement programming |
US6289248B1 (en) | 1999-08-20 | 2001-09-11 | Cardiac Pacemakers, Inc. | System and method for detecting and displaying parameter interactions |
US6454705B1 (en) | 1999-09-21 | 2002-09-24 | Cardiocom | Medical wellness parameters management system, apparatus and method |
EP1217942A1 (en) | 1999-09-24 | 2002-07-03 | Healthetech, Inc. | Physiological monitor and associated computation, display and communication unit |
US7127290B2 (en) | 1999-10-01 | 2006-10-24 | Cardiac Pacemakers, Inc. | Cardiac rhythm management systems and methods predicting congestive heart failure status |
AU8007600A (en) | 1999-10-08 | 2001-04-23 | Healthetech, Inc. | Monitoring caloric expenditure rate and caloric diet |
US6453201B1 (en) | 1999-10-20 | 2002-09-17 | Cardiac Pacemakers, Inc. | Implantable medical device with voice responding and recording capacity |
US6409675B1 (en) * | 1999-11-10 | 2002-06-25 | Pacesetter, Inc. | Extravascular hemodynamic monitor |
US6336903B1 (en) * | 1999-11-16 | 2002-01-08 | Cardiac Intelligence Corp. | Automated collection and analysis patient care system and method for diagnosing and monitoring congestive heart failure and outcomes thereof |
US6752765B1 (en) | 1999-12-01 | 2004-06-22 | Medtronic, Inc. | Method and apparatus for monitoring heart rate and abnormal respiration |
US6513532B2 (en) * | 2000-01-19 | 2003-02-04 | Healthetech, Inc. | Diet and activity-monitoring device |
EP1118307B1 (en) * | 2000-01-19 | 2007-10-24 | Pacesetter, Inc. | An implantable cardiac device for monitoring progression or regression of heart disease |
US6438407B1 (en) | 2000-03-20 | 2002-08-20 | Medtronic, Inc. | Method and apparatus for monitoring physiologic parameters conjunction with a treatment |
US6482158B2 (en) | 2000-05-19 | 2002-11-19 | Healthetech, Inc. | System and method of ultrasonic mammography |
US6659968B1 (en) * | 2000-06-01 | 2003-12-09 | Advanced Bionics Corporation | Activity monitor for pain management efficacy measurement |
EP1358745B1 (en) * | 2000-08-22 | 2008-12-10 | Medtronic, Inc. | Medical device systems implemented network system for remote patient management |
US6607387B2 (en) | 2000-10-30 | 2003-08-19 | Healthetech, Inc. | Sensor system for diagnosing dental conditions |
US20020055857A1 (en) * | 2000-10-31 | 2002-05-09 | Mault James R. | Method of assisting individuals in lifestyle control programs conducive to good health |
DE60035719T2 (en) | 2000-11-17 | 2008-04-30 | Medtronic, Inc., Minneapolis | Device for monitoring heart rate and abnormal ventilation |
US6741885B1 (en) | 2000-12-07 | 2004-05-25 | Pacesetter, Inc. | Implantable cardiac device for managing the progression of heart disease and method |
US7181285B2 (en) | 2000-12-26 | 2007-02-20 | Cardiac Pacemakers, Inc. | Expert system and method |
US7052466B2 (en) | 2001-04-11 | 2006-05-30 | Cardiac Pacemakers, Inc. | Apparatus and method for outputting heart sounds |
US6636762B2 (en) | 2001-04-30 | 2003-10-21 | Medtronic, Inc. | Method and system for monitoring heart failure using rate change dynamics |
US6675044B2 (en) * | 2001-05-07 | 2004-01-06 | Medtronic, Inc. | Software-based record management system with access to time-line ordered clinical data acquired by an implanted device |
EP1256316A1 (en) * | 2001-05-07 | 2002-11-13 | Move2Health B.V. | Portable device comprising an acceleration sensor and method of generating instructions or advice |
US20030050566A1 (en) * | 2001-09-07 | 2003-03-13 | Medtronic, Inc. | Arrhythmia notification |
US8224663B2 (en) * | 2002-05-24 | 2012-07-17 | Becton, Dickinson And Company | System and method for assessment and corrective action based on guidelines |
US7383088B2 (en) * | 2001-11-07 | 2008-06-03 | Cardiac Pacemakers, Inc. | Centralized management system for programmable medical devices |
NO20016385L (en) * | 2001-12-27 | 2003-06-30 | Medinnova Sf | System for monitoring heart rate changes, preferably a heart muscle |
US20030125662A1 (en) * | 2002-01-03 | 2003-07-03 | Tuan Bui | Method and apparatus for providing medical treatment therapy based on calculated demand |
US6980112B2 (en) * | 2002-01-08 | 2005-12-27 | International Business Machines Corporation | Emergency call patient locating system for implanted automatic defibrillators |
US8043213B2 (en) * | 2002-12-18 | 2011-10-25 | Cardiac Pacemakers, Inc. | Advanced patient management for triaging health-related data using color codes |
US8391989B2 (en) | 2002-12-18 | 2013-03-05 | Cardiac Pacemakers, Inc. | Advanced patient management for defining, identifying and using predetermined health-related events |
US7043305B2 (en) | 2002-03-06 | 2006-05-09 | Cardiac Pacemakers, Inc. | Method and apparatus for establishing context among events and optimizing implanted medical device performance |
US7468032B2 (en) | 2002-12-18 | 2008-12-23 | Cardiac Pacemakers, Inc. | Advanced patient management for identifying, displaying and assisting with correlating health-related data |
US20040122487A1 (en) | 2002-12-18 | 2004-06-24 | John Hatlestad | Advanced patient management with composite parameter indices |
US20040122296A1 (en) * | 2002-12-18 | 2004-06-24 | John Hatlestad | Advanced patient management for triaging health-related data |
US7983759B2 (en) | 2002-12-18 | 2011-07-19 | Cardiac Pacemakers, Inc. | Advanced patient management for reporting multiple health-related parameters |
US20040122486A1 (en) * | 2002-12-18 | 2004-06-24 | Stahmann Jeffrey E. | Advanced patient management for acquiring, trending and displaying health-related parameters |
US20040122294A1 (en) | 2002-12-18 | 2004-06-24 | John Hatlestad | Advanced patient management with environmental data |
US7039462B2 (en) * | 2002-06-14 | 2006-05-02 | Cardiac Pacemakers, Inc. | Method and apparatus for detecting oscillations in cardiac rhythm |
US7113825B2 (en) | 2002-05-03 | 2006-09-26 | Cardiac Pacemakers, Inc. | Method and apparatus for detecting acoustic oscillations in cardiac rhythm |
US7110823B2 (en) * | 2002-06-11 | 2006-09-19 | Advanced Bionics Corporation | RF telemetry link for establishment and maintenance of communications with an implantable device |
US20030126593A1 (en) * | 2002-11-04 | 2003-07-03 | Mault James R. | Interactive physiological monitoring system |
US7072711B2 (en) | 2002-11-12 | 2006-07-04 | Cardiac Pacemakers, Inc. | Implantable device for delivering cardiac drug therapy |
US7986994B2 (en) | 2002-12-04 | 2011-07-26 | Medtronic, Inc. | Method and apparatus for detecting change in intrathoracic electrical impedance |
US7191006B2 (en) * | 2002-12-05 | 2007-03-13 | Cardiac Pacemakers, Inc. | Cardiac rhythm management systems and methods for rule-illustrative parameter entry |
AU2003280131A1 (en) * | 2002-12-10 | 2004-06-30 | Koninklijke Philips Electronics N.V. | Activity monitoring |
WO2004052203A1 (en) * | 2002-12-10 | 2004-06-24 | Koninklijke Philips Electronics N.V. | Activity monitoring |
US20050080348A1 (en) * | 2003-09-18 | 2005-04-14 | Stahmann Jeffrey E. | Medical event logbook system and method |
US8951205B2 (en) | 2002-12-30 | 2015-02-10 | Cardiac Pacemakers, Inc. | Method and apparatus for detecting atrial filling pressure |
US7972275B2 (en) | 2002-12-30 | 2011-07-05 | Cardiac Pacemakers, Inc. | Method and apparatus for monitoring of diastolic hemodynamics |
US7378955B2 (en) | 2003-01-03 | 2008-05-27 | Cardiac Pacemakers, Inc. | System and method for correlating biometric trends with a related temporal event |
US7136707B2 (en) | 2003-01-21 | 2006-11-14 | Cardiac Pacemakers, Inc. | Recordable macros for pacemaker follow-up |
US6915157B2 (en) * | 2003-02-18 | 2005-07-05 | Medtronic, Inc. | Implantable medical device for assessing heart failure state from Mechanical Pulsus Alternans |
US6887207B2 (en) | 2003-02-26 | 2005-05-03 | Medtronic, Inc. | Methods and apparatus for estimation of ventricular afterload based on ventricular pressure measurements |
US20040199056A1 (en) * | 2003-04-03 | 2004-10-07 | International Business Machines Corporation | Body monitoring using local area wireless interfaces |
US20040230456A1 (en) * | 2003-05-14 | 2004-11-18 | Lozier Luke R. | System for identifying candidates for ICD implantation |
US7477932B2 (en) * | 2003-05-28 | 2009-01-13 | Cardiac Pacemakers, Inc. | Cardiac waveform template creation, maintenance and use |
US7539803B2 (en) * | 2003-06-13 | 2009-05-26 | Agere Systems Inc. | Bi-directional interface for low data rate application |
US7177684B1 (en) | 2003-07-03 | 2007-02-13 | Pacesetter, Inc. | Activity monitor and six-minute walk test for depression and CHF patients |
US8251061B2 (en) * | 2003-09-18 | 2012-08-28 | Cardiac Pacemakers, Inc. | Methods and systems for control of gas therapy |
US7575553B2 (en) * | 2003-09-18 | 2009-08-18 | Cardiac Pacemakers, Inc. | Methods and systems for assessing pulmonary disease |
US8002553B2 (en) | 2003-08-18 | 2011-08-23 | Cardiac Pacemakers, Inc. | Sleep quality data collection and evaluation |
US7678061B2 (en) | 2003-09-18 | 2010-03-16 | Cardiac Pacemakers, Inc. | System and method for characterizing patient respiration |
US7572225B2 (en) * | 2003-09-18 | 2009-08-11 | Cardiac Pacemakers, Inc. | Sleep logbook |
US7610094B2 (en) * | 2003-09-18 | 2009-10-27 | Cardiac Pacemakers, Inc. | Synergistic use of medical devices for detecting medical disorders |
US7967756B2 (en) * | 2003-09-18 | 2011-06-28 | Cardiac Pacemakers, Inc. | Respiratory therapy control based on cardiac cycle |
US7396333B2 (en) * | 2003-08-18 | 2008-07-08 | Cardiac Pacemakers, Inc. | Prediction of disordered breathing |
US7887493B2 (en) | 2003-09-18 | 2011-02-15 | Cardiac Pacemakers, Inc. | Implantable device employing movement sensing for detecting sleep-related disorders |
US7668591B2 (en) * | 2003-09-18 | 2010-02-23 | Cardiac Pacemakers, Inc. | Automatic activation of medical processes |
US7662101B2 (en) * | 2003-09-18 | 2010-02-16 | Cardiac Pacemakers, Inc. | Therapy control based on cardiopulmonary status |
US20050142070A1 (en) * | 2003-09-18 | 2005-06-30 | Hartley Jesse W. | Methods and systems for assessing pulmonary disease with drug therapy control |
EP1670547B1 (en) | 2003-08-18 | 2008-11-12 | Cardiac Pacemakers, Inc. | Patient monitoring system |
US8396565B2 (en) * | 2003-09-15 | 2013-03-12 | Medtronic, Inc. | Automatic therapy adjustments |
US7286872B2 (en) * | 2003-10-07 | 2007-10-23 | Cardiac Pacemakers, Inc. | Method and apparatus for managing data from multiple sensing channels |
US7937149B2 (en) * | 2003-12-03 | 2011-05-03 | Medtronic, Inc. | Method and apparatus for detecting change in physiologic parameters |
US20050137629A1 (en) * | 2003-12-08 | 2005-06-23 | Dyjach John A. | Trended measurement of cardiac resynchronization therapy |
US20060247693A1 (en) | 2005-04-28 | 2006-11-02 | Yanting Dong | Non-captured intrinsic discrimination in cardiac pacing response classification |
US7319900B2 (en) * | 2003-12-11 | 2008-01-15 | Cardiac Pacemakers, Inc. | Cardiac response classification using multiple classification windows |
US8521284B2 (en) | 2003-12-12 | 2013-08-27 | Cardiac Pacemakers, Inc. | Cardiac response classification using multisite sensing and pacing |
US7774064B2 (en) * | 2003-12-12 | 2010-08-10 | Cardiac Pacemakers, Inc. | Cardiac response classification using retriggerable classification windows |
US8589174B2 (en) * | 2003-12-16 | 2013-11-19 | Adventium Enterprises | Activity monitoring |
US7471980B2 (en) * | 2003-12-22 | 2008-12-30 | Cardiac Pacemakers, Inc. | Synchronizing continuous signals and discrete events for an implantable medical device |
US7115096B2 (en) * | 2003-12-24 | 2006-10-03 | Cardiac Pacemakers, Inc. | Third heart sound activity index for heart failure monitoring |
ATE536801T1 (en) * | 2004-01-15 | 2011-12-15 | Koninkl Philips Electronics Nv | ADAPTIVE PHYSIOLOGICAL MONITORING SYSTEM AND METHOD OF USE THEREOF |
US7232435B2 (en) * | 2004-02-06 | 2007-06-19 | Medtronic, Inc. | Delivery of a sympatholytic cardiovascular agent to the central nervous system to counter heart failure and pathologies associated with heart failure |
US20050192487A1 (en) * | 2004-02-27 | 2005-09-01 | Cosentino Louis C. | System for collection, manipulation, and analysis of data from remote health care devices |
US8894576B2 (en) * | 2004-03-10 | 2014-11-25 | University Of Virginia Patent Foundation | System and method for the inference of activities of daily living and instrumental activities of daily living automatically |
US8308661B2 (en) | 2004-03-16 | 2012-11-13 | Medtronic, Inc. | Collecting activity and sleep quality information via a medical device |
US7395113B2 (en) * | 2004-03-16 | 2008-07-01 | Medtronic, Inc. | Collecting activity information to evaluate therapy |
US8055348B2 (en) | 2004-03-16 | 2011-11-08 | Medtronic, Inc. | Detecting sleep to evaluate therapy |
US8725244B2 (en) * | 2004-03-16 | 2014-05-13 | Medtronic, Inc. | Determination of sleep quality for neurological disorders |
US20070276439A1 (en) * | 2004-03-16 | 2007-11-29 | Medtronic, Inc. | Collecting sleep quality information via a medical device |
US7542803B2 (en) * | 2004-03-16 | 2009-06-02 | Medtronic, Inc. | Sensitivity analysis for selecting therapy parameter sets |
US7805196B2 (en) | 2004-03-16 | 2010-09-28 | Medtronic, Inc. | Collecting activity information to evaluate therapy |
EP1849412B1 (en) * | 2004-03-16 | 2009-03-04 | Medtronic, Inc. | Collecting activity information to evaluate therapy |
US20050209512A1 (en) * | 2004-03-16 | 2005-09-22 | Heruth Kenneth T | Detecting sleep |
US7366572B2 (en) * | 2004-03-16 | 2008-04-29 | Medtronic, Inc. | Controlling therapy based on sleep quality |
US7491181B2 (en) * | 2004-03-16 | 2009-02-17 | Medtronic, Inc. | Collecting activity and sleep quality information via a medical device |
US7330760B2 (en) * | 2004-03-16 | 2008-02-12 | Medtronic, Inc. | Collecting posture information to evaluate therapy |
US7792583B2 (en) | 2004-03-16 | 2010-09-07 | Medtronic, Inc. | Collecting posture information to evaluate therapy |
US7717848B2 (en) * | 2004-03-16 | 2010-05-18 | Medtronic, Inc. | Collecting sleep quality information via a medical device |
US7881798B2 (en) | 2004-03-16 | 2011-02-01 | Medtronic Inc. | Controlling therapy based on sleep quality |
US7313440B2 (en) * | 2004-04-14 | 2007-12-25 | Medtronic, Inc. | Collecting posture and activity information to evaluate therapy |
US8135473B2 (en) | 2004-04-14 | 2012-03-13 | Medtronic, Inc. | Collecting posture and activity information to evaluate therapy |
US7031766B1 (en) | 2004-04-20 | 2006-04-18 | Pacesetter, Inc. | Methods and devices for determining exercise diagnostic parameters |
US7676262B1 (en) | 2004-04-20 | 2010-03-09 | Pacesetter, Inc. | Methods and devices for determining exercise compliance diagnostics |
US7043294B1 (en) | 2004-04-20 | 2006-05-09 | Pacesetter, Inc. | Methods and devices for determining heart rate recovery |
US7171271B2 (en) * | 2004-05-11 | 2007-01-30 | Pacesetter, Inc. | System and method for evaluating heart failure using an implantable medical device based on heart rate during patient activity |
US7548785B2 (en) * | 2004-06-10 | 2009-06-16 | Pacesetter, Inc. | Collecting and analyzing sensed information as a trend of heart failure progression or regression |
US7559901B2 (en) * | 2004-07-28 | 2009-07-14 | Cardiac Pacemakers, Inc. | Determining a patient's posture from mechanical vibrations of the heart |
US7269458B2 (en) | 2004-08-09 | 2007-09-11 | Cardiac Pacemakers, Inc. | Cardiopulmonary functional status assessment via heart rate response detection by implantable cardiac device |
US7389143B2 (en) * | 2004-08-12 | 2008-06-17 | Cardiac Pacemakers, Inc. | Cardiopulmonary functional status assessment via metabolic response detection by implantable cardiac device |
AU2005304912A1 (en) * | 2004-11-04 | 2006-05-18 | Smith & Nephew, Inc. | Cycle and load measurement device |
US7373820B1 (en) | 2004-11-23 | 2008-05-20 | James Terry L | Accelerometer for data collection and communication |
US7662104B2 (en) | 2005-01-18 | 2010-02-16 | Cardiac Pacemakers, Inc. | Method for correction of posture dependence on heart sounds |
US7386345B2 (en) * | 2005-01-27 | 2008-06-10 | Cardiac Pacemakers, Inc. | Apparatus and method for temporary treatment of acute heart failure decompensation |
US7708693B2 (en) * | 2005-01-27 | 2010-05-04 | Medtronic, Inc. | System and method for detecting artifactual hemodynamic waveform data |
US7367951B2 (en) * | 2005-01-27 | 2008-05-06 | Medtronic, Inc. | System and method for detecting cardiovascular health conditions using hemodynamic pressure waveforms |
US7680534B2 (en) * | 2005-02-28 | 2010-03-16 | Cardiac Pacemakers, Inc. | Implantable cardiac device with dyspnea measurement |
US7526335B2 (en) * | 2005-03-10 | 2009-04-28 | Medtronic, Inc. | Communications system for an implantable device and a drug dispenser |
US7392086B2 (en) | 2005-04-26 | 2008-06-24 | Cardiac Pacemakers, Inc. | Implantable cardiac device and method for reduced phrenic nerve stimulation |
US7499751B2 (en) * | 2005-04-28 | 2009-03-03 | Cardiac Pacemakers, Inc. | Cardiac signal template generation using waveform clustering |
WO2006119186A2 (en) * | 2005-05-02 | 2006-11-09 | University Of Virginia Patent Foundation | Systems, devices, and methods for interpreting movement |
US7404802B2 (en) * | 2005-05-05 | 2008-07-29 | Cardiac Pacemakers, Inc. | Trending of systolic murmur intensity for monitoring cardiac disease with implantable device |
US7670298B2 (en) * | 2005-06-01 | 2010-03-02 | Cardiac Pacemakers, Inc. | Sensing rate of change of pressure in the left ventricle with an implanted device |
US8021299B2 (en) * | 2005-06-01 | 2011-09-20 | Medtronic, Inc. | Correlating a non-polysomnographic physiological parameter set with sleep states |
US8972002B2 (en) | 2005-06-01 | 2015-03-03 | Cardiac Pacemakers, Inc. | Remote closed-loop titration of decongestive therapy for the treatment of advanced heart failure |
US7922669B2 (en) | 2005-06-08 | 2011-04-12 | Cardiac Pacemakers, Inc. | Ischemia detection using a heart sound sensor |
US20070021678A1 (en) * | 2005-07-19 | 2007-01-25 | Cardiac Pacemakers, Inc. | Methods and apparatus for monitoring physiological responses to steady state activity |
US7585279B2 (en) | 2005-07-26 | 2009-09-08 | Cardiac Pacemakers, Inc. | Managing preload reserve by tracking the ventricular operating point with heart sounds |
US7634309B2 (en) * | 2005-08-19 | 2009-12-15 | Cardiac Pacemakers, Inc. | Tracking progression of congestive heart failure via a force-frequency relationship |
US20070073590A1 (en) * | 2005-08-22 | 2007-03-29 | Cosentino Louis C | Remote monitor for physiological parameters and durable medical supplies |
WO2007025191A1 (en) | 2005-08-23 | 2007-03-01 | Smith & Nephew, Inc. | Telemetric orthopaedic implant |
US20070055115A1 (en) * | 2005-09-08 | 2007-03-08 | Jonathan Kwok | Characterization of sleep disorders using composite patient data |
US9168383B2 (en) | 2005-10-14 | 2015-10-27 | Pacesetter, Inc. | Leadless cardiac pacemaker with conducted communication |
US9358400B2 (en) | 2005-10-14 | 2016-06-07 | Pacesetter, Inc. | Leadless cardiac pacemaker |
AT502921B1 (en) * | 2005-10-21 | 2012-01-15 | Falko Dr Skrabal | DEVICE FOR MEASURING HEART AND VESSEL FUNCTION (FUNCTION) AND BODY SPACES (SPACES) BY MEANS OF IMPEDANCE MEASUREMENT |
US7574255B1 (en) * | 2005-11-07 | 2009-08-11 | Pacesetter, Inc. | Criteria for monitoring intrathoracic impedance |
US7774055B1 (en) | 2005-11-07 | 2010-08-10 | Pacesetter, Inc. | Left atrial pressure-based criteria for monitoring intrathoracic impedance |
US8108034B2 (en) | 2005-11-28 | 2012-01-31 | Cardiac Pacemakers, Inc. | Systems and methods for valvular regurgitation detection |
US8016776B2 (en) * | 2005-12-02 | 2011-09-13 | Medtronic, Inc. | Wearable ambulatory data recorder |
US7957809B2 (en) | 2005-12-02 | 2011-06-07 | Medtronic, Inc. | Closed-loop therapy adjustment |
US7785256B1 (en) | 2006-01-11 | 2010-08-31 | Pacesetter, Inc. | Method and system for displaying patient activity data using Poincaré and intensity plot |
US7925344B2 (en) * | 2006-01-20 | 2011-04-12 | Medtronic, Inc. | System and method of using AV conduction timing |
US7671733B2 (en) * | 2006-03-17 | 2010-03-02 | Koninklijke Philips Electronics N.V. | Method and system for medical alarm monitoring, reporting and normalization |
US8744587B2 (en) | 2006-03-24 | 2014-06-03 | Medtronic, Inc. | Collecting gait information for evaluation and control of therapy |
US7780606B2 (en) * | 2006-03-29 | 2010-08-24 | Cardiac Pacemakers, Inc. | Hemodynamic stability assessment based on heart sounds |
US7613672B2 (en) | 2006-04-27 | 2009-11-03 | Cardiac Pacemakers, Inc. | Medical device user interface automatically resolving interaction between programmable parameters |
EP2036364A4 (en) * | 2006-05-17 | 2013-02-06 | 24Eight Llc | Method and apparatus for mobility analysis using real-time acceleration data |
US8294716B2 (en) * | 2006-05-31 | 2012-10-23 | Koninklijke Philips Electronics N.V. | Display of trends and anticipated trends from mitigation |
US8000780B2 (en) | 2006-06-27 | 2011-08-16 | Cardiac Pacemakers, Inc. | Detection of myocardial ischemia from the time sequence of implanted sensor measurements |
US20080071185A1 (en) * | 2006-08-08 | 2008-03-20 | Cardiac Pacemakers, Inc. | Periodic breathing during activity |
US8226570B2 (en) | 2006-08-08 | 2012-07-24 | Cardiac Pacemakers, Inc. | Respiration monitoring for heart failure using implantable device |
US8209013B2 (en) | 2006-09-14 | 2012-06-26 | Cardiac Pacemakers, Inc. | Therapeutic electrical stimulation that avoids undesirable activation |
US20080119749A1 (en) * | 2006-11-20 | 2008-05-22 | Cardiac Pacemakers, Inc. | Respiration-synchronized heart sound trending |
US8096954B2 (en) * | 2006-11-29 | 2012-01-17 | Cardiac Pacemakers, Inc. | Adaptive sampling of heart sounds |
US20080161651A1 (en) * | 2006-12-27 | 2008-07-03 | Cardiac Pacemakers, Inc. | Surrogate measure of patient compliance |
US7736319B2 (en) * | 2007-01-19 | 2010-06-15 | Cardiac Pacemakers, Inc. | Ischemia detection using heart sound timing |
US8014863B2 (en) * | 2007-01-19 | 2011-09-06 | Cardiac Pacemakers, Inc. | Heart attack or ischemia detector |
WO2008103181A1 (en) | 2007-02-23 | 2008-08-28 | Smith & Nephew, Inc. | Processing sensed accelerometer data for determination of bone healing |
US8068918B2 (en) * | 2007-03-09 | 2011-11-29 | Enteromedics Inc. | Remote monitoring and control of implantable devices |
US20080228093A1 (en) * | 2007-03-13 | 2008-09-18 | Yanting Dong | Systems and methods for enhancing cardiac signal features used in morphology discrimination |
US7844336B2 (en) | 2007-04-10 | 2010-11-30 | Cardiac Pacemakers, Inc. | Implantable medical device configured as a pedometer |
US7853327B2 (en) | 2007-04-17 | 2010-12-14 | Cardiac Pacemakers, Inc. | Heart sound tracking system and method |
US8831714B2 (en) * | 2007-05-07 | 2014-09-09 | Cardiac Pacemakers, Inc. | Apparatus and method for heart failure indication based on heart rate, onset and tachyarrhythmia |
US20080281165A1 (en) * | 2007-05-09 | 2008-11-13 | Raghu Rai | system and method for acquiring and transferring data to a remote server |
US20080300657A1 (en) * | 2007-05-31 | 2008-12-04 | Mark Raymond Stultz | Therapy system |
US20080306762A1 (en) * | 2007-06-08 | 2008-12-11 | James Terry L | System and Method for Managing Absenteeism in an Employee Environment |
US9743859B2 (en) | 2007-06-15 | 2017-08-29 | Cardiac Pacemakers, Inc. | Daytime/nighttime respiration rate monitoring |
US7530956B2 (en) | 2007-06-15 | 2009-05-12 | Cardiac Pacemakers, Inc. | Daytime/nighttime respiration rate monitoring |
US20090018404A1 (en) * | 2007-07-12 | 2009-01-15 | Cardiac Pacemakers, Inc. | Cardiovascular Autonomic Neuropathy Testing Utilizing an Implantable Medical Device |
DE102007034042A1 (en) * | 2007-07-20 | 2009-01-22 | Biotronik Crm Patent Ag | Implantable medical device |
US8043215B2 (en) * | 2007-08-07 | 2011-10-25 | Cardiac Pacemakers, Inc. | Drug titration utilizing an implantable medical device |
US8265736B2 (en) * | 2007-08-07 | 2012-09-11 | Cardiac Pacemakers, Inc. | Method and apparatus to perform electrode combination selection |
US9037239B2 (en) | 2007-08-07 | 2015-05-19 | Cardiac Pacemakers, Inc. | Method and apparatus to perform electrode combination selection |
US20090048493A1 (en) * | 2007-08-17 | 2009-02-19 | James Terry L | Health and Entertainment Device for Collecting, Converting, Displaying and Communicating Data |
EP2191534B1 (en) | 2007-09-06 | 2016-10-26 | Smith & Nephew, Inc. | System and method for communicating with a telemetric implant |
US9254100B2 (en) * | 2007-09-12 | 2016-02-09 | Cardiac Pacemakers, Inc. | Logging daily average metabolic activity using a motion sensor |
US8897868B2 (en) | 2007-09-14 | 2014-11-25 | Medtronic, Inc. | Medical device automatic start-up upon contact to patient tissue |
US8591430B2 (en) | 2007-09-14 | 2013-11-26 | Corventis, Inc. | Adherent device for respiratory monitoring |
EP2200499B1 (en) | 2007-09-14 | 2019-05-01 | Medtronic Monitoring, Inc. | Multi-sensor patient monitor to detect impending cardiac decompensation |
US8116841B2 (en) | 2007-09-14 | 2012-02-14 | Corventis, Inc. | Adherent device with multiple physiological sensors |
US8460189B2 (en) | 2007-09-14 | 2013-06-11 | Corventis, Inc. | Adherent cardiac monitor with advanced sensing capabilities |
WO2009036333A1 (en) | 2007-09-14 | 2009-03-19 | Corventis, Inc. | Dynamic pairing of patients to data collection gateways |
US8684925B2 (en) | 2007-09-14 | 2014-04-01 | Corventis, Inc. | Injectable device for physiological monitoring |
US8121689B2 (en) | 2007-10-01 | 2012-02-21 | Cardiac Pacemakers, Inc. | Proactive interactive limits override for implantable medical device user interface |
JP2011501276A (en) * | 2007-10-12 | 2011-01-06 | ペイシェンツライクミー, インコーポレイテッド | Self-improvement methods using online communities to predict health-related outcomes |
US7676332B2 (en) * | 2007-12-27 | 2010-03-09 | Kersh Risk Management, Inc. | System and method for processing raw activity energy expenditure data |
US9020780B2 (en) * | 2007-12-31 | 2015-04-28 | The Nielsen Company (Us), Llc | Motion detector module |
US8915866B2 (en) * | 2008-01-18 | 2014-12-23 | Warsaw Orthopedic, Inc. | Implantable sensor and associated methods |
US8986253B2 (en) | 2008-01-25 | 2015-03-24 | Tandem Diabetes Care, Inc. | Two chamber pumps and related methods |
US20110004076A1 (en) * | 2008-02-01 | 2011-01-06 | Smith & Nephew, Inc. | System and method for communicating with an implant |
US20090204422A1 (en) * | 2008-02-12 | 2009-08-13 | James Terry L | System and Method for Remotely Updating a Health Station |
CN101939051B (en) | 2008-02-14 | 2013-07-10 | 心脏起搏器公司 | Method and apparatus for phrenic stimulation detection |
WO2009110996A1 (en) * | 2008-03-05 | 2009-09-11 | Cardiac Pacemakers, Inc. | Automated heart function classification to standardized classes |
WO2009114548A1 (en) | 2008-03-12 | 2009-09-17 | Corventis, Inc. | Heart failure decompensation prediction based on cardiac rhythm |
EP2252209B1 (en) * | 2008-03-14 | 2012-01-18 | Koninklijke Philips Electronics N.V. | An activity monitoring system insensitive to accelerations induced by external motion factors |
US8412317B2 (en) | 2008-04-18 | 2013-04-02 | Corventis, Inc. | Method and apparatus to measure bioelectric impedance of patient tissue |
US9320448B2 (en) | 2008-04-18 | 2016-04-26 | Pacesetter, Inc. | Systems and methods for improved atrial fibrillation (AF) monitoring |
US8165840B2 (en) | 2008-06-12 | 2012-04-24 | Cardiac Pacemakers, Inc. | Posture sensor automatic calibration |
US9050471B2 (en) | 2008-07-11 | 2015-06-09 | Medtronic, Inc. | Posture state display on medical device user interface |
US8958885B2 (en) * | 2008-07-11 | 2015-02-17 | Medtronic, Inc. | Posture state classification for a medical device |
US9440084B2 (en) * | 2008-07-11 | 2016-09-13 | Medtronic, Inc. | Programming posture responsive therapy |
US8231556B2 (en) * | 2008-07-11 | 2012-07-31 | Medtronic, Inc. | Obtaining baseline patient information |
US8437861B2 (en) * | 2008-07-11 | 2013-05-07 | Medtronic, Inc. | Posture state redefinition based on posture data and therapy adjustments |
US8708934B2 (en) * | 2008-07-11 | 2014-04-29 | Medtronic, Inc. | Reorientation of patient posture states for posture-responsive therapy |
US8200340B2 (en) * | 2008-07-11 | 2012-06-12 | Medtronic, Inc. | Guided programming for posture-state responsive therapy |
US8504150B2 (en) | 2008-07-11 | 2013-08-06 | Medtronic, Inc. | Associating therapy adjustments with posture states using a stability timer |
US8447411B2 (en) | 2008-07-11 | 2013-05-21 | Medtronic, Inc. | Patient interaction with posture-responsive therapy |
US20100016742A1 (en) * | 2008-07-19 | 2010-01-21 | James Terry L | System and Method for Monitoring, Measuring, and Addressing Stress |
WO2010011678A1 (en) * | 2008-07-21 | 2010-01-28 | Seattle Information Systems, Inc. | Person reported outcome report generation |
US8712509B2 (en) * | 2008-07-25 | 2014-04-29 | Medtronic, Inc. | Virtual physician acute myocardial infarction detection system and method |
US9713701B2 (en) | 2008-07-31 | 2017-07-25 | Medtronic, Inc. | Using multiple diagnostic parameters for predicting heart failure events |
US8255046B2 (en) * | 2008-07-31 | 2012-08-28 | Medtronic, Inc. | Detecting worsening heart failure based on impedance measurements |
US8280517B2 (en) | 2008-09-19 | 2012-10-02 | Medtronic, Inc. | Automatic validation techniques for validating operation of medical devices |
AU2009293019A1 (en) | 2008-09-19 | 2010-03-25 | Tandem Diabetes Care Inc. | Solute concentration measurement device and related methods |
US8632473B2 (en) * | 2009-01-30 | 2014-01-21 | Medtronic, Inc. | Detecting worsening heart failure based on fluid accumulation with respiratory confirmation |
US8527068B2 (en) | 2009-02-02 | 2013-09-03 | Nanostim, Inc. | Leadless cardiac pacemaker with secondary fixation capability |
US8152694B2 (en) * | 2009-03-16 | 2012-04-10 | Robert Bosch Gmbh | Activity monitoring device and method |
US8326426B2 (en) * | 2009-04-03 | 2012-12-04 | Enteromedics, Inc. | Implantable device with heat storage |
US9026223B2 (en) | 2009-04-30 | 2015-05-05 | Medtronic, Inc. | Therapy system including multiple posture sensors |
US8175720B2 (en) | 2009-04-30 | 2012-05-08 | Medtronic, Inc. | Posture-responsive therapy control based on patient input |
EP2430574A1 (en) | 2009-04-30 | 2012-03-21 | Patientslikeme, Inc. | Systems and methods for encouragement of data submission in online communities |
US9327070B2 (en) * | 2009-04-30 | 2016-05-03 | Medtronic, Inc. | Medical device therapy based on posture and timing |
JP2012525952A (en) * | 2009-06-03 | 2012-10-25 | カーディアック ペースメイカーズ, インコーポレイテッド | System and method for monitoring cardiovascular pressure |
BRPI1016004A2 (en) * | 2009-06-30 | 2016-04-26 | Lifescan Inc | methods for testing analytes and device for calculating basal insulin therapy. |
EP2455875A3 (en) * | 2009-06-30 | 2013-01-16 | Lifescan Scotland Limited | System and method for diabetes management |
EP3284494A1 (en) | 2009-07-30 | 2018-02-21 | Tandem Diabetes Care, Inc. | Portable infusion pump system |
BR112012007134A2 (en) * | 2009-09-29 | 2016-08-23 | Lifescan Scotland Ltd | diabetes control analyte test device and method |
WO2011050283A2 (en) | 2009-10-22 | 2011-04-28 | Corventis, Inc. | Remote detection and monitoring of functional chronotropic incompetence |
US20110106201A1 (en) * | 2009-10-30 | 2011-05-05 | Sourav Bhunia | Implantable heart failure monitor |
US8271072B2 (en) * | 2009-10-30 | 2012-09-18 | Medtronic, Inc. | Detecting worsening heart failure |
US9451897B2 (en) | 2009-12-14 | 2016-09-27 | Medtronic Monitoring, Inc. | Body adherent patch with electronics for physiologic monitoring |
US8758274B2 (en) * | 2010-01-08 | 2014-06-24 | Medtronic, Inc. | Automated adjustment of posture state definitions for a medical device |
US9956418B2 (en) | 2010-01-08 | 2018-05-01 | Medtronic, Inc. | Graphical manipulation of posture zones for posture-responsive therapy |
US9357949B2 (en) | 2010-01-08 | 2016-06-07 | Medtronic, Inc. | User interface that displays medical therapy and posture data |
US8579834B2 (en) * | 2010-01-08 | 2013-11-12 | Medtronic, Inc. | Display of detected patient posture state |
US8257289B2 (en) * | 2010-02-03 | 2012-09-04 | Tyco Healthcare Group Lp | Fitting of compression garment |
JP5588020B2 (en) * | 2010-02-16 | 2014-09-10 | カーディアック ペースメイカーズ, インコーポレイテッド | Dynamics of physiological responses to movements in daily life movements |
EP2590098B1 (en) * | 2010-02-25 | 2014-11-05 | Lifescan Scotland Limited | Analyte testing method and system with high and low blood glucose trends notification |
US8965498B2 (en) | 2010-04-05 | 2015-02-24 | Corventis, Inc. | Method and apparatus for personalized physiologic parameters |
US9566441B2 (en) | 2010-04-30 | 2017-02-14 | Medtronic, Inc. | Detecting posture sensor signal shift or drift in medical devices |
US20120083712A1 (en) | 2010-09-30 | 2012-04-05 | Tyco Healthcare Group Lp | Monitoring Compliance Using Venous Refill Detection |
EP2627403A4 (en) | 2010-10-12 | 2014-03-26 | Nanostim Inc | Temperature sensor for a leadless cardiac pacemaker |
US9060692B2 (en) | 2010-10-12 | 2015-06-23 | Pacesetter, Inc. | Temperature sensor for a leadless cardiac pacemaker |
JP2013540022A (en) | 2010-10-13 | 2013-10-31 | ナノスティム・インコーポレイテッド | Leadless cardiac pacemaker with screw anti-rotation element |
US8585604B2 (en) | 2010-10-29 | 2013-11-19 | Medtronic, Inc. | Integrated patient care |
JP2014501136A (en) | 2010-12-13 | 2014-01-20 | ナノスティム・インコーポレイテッド | Delivery catheter system and method |
WO2012082755A1 (en) | 2010-12-13 | 2012-06-21 | Nanostim, Inc. | Pacemaker retrieval systems and methods |
EP2654889B1 (en) | 2010-12-20 | 2017-03-01 | Pacesetter, Inc. | Leadless pacemaker with radial fixation mechanism |
US10098584B2 (en) | 2011-02-08 | 2018-10-16 | Cardiac Pacemakers, Inc. | Patient health improvement monitor |
US9069380B2 (en) | 2011-06-10 | 2015-06-30 | Aliphcom | Media device, application, and content management using sensory input |
US8818505B2 (en) | 2011-09-28 | 2014-08-26 | Medtronic, Inc. | Physiological perturbations for measuring heart failure |
WO2013067496A2 (en) | 2011-11-04 | 2013-05-10 | Nanostim, Inc. | Leadless cardiac pacemaker with integral battery and redundant welds |
US9907959B2 (en) | 2012-04-12 | 2018-03-06 | Medtronic, Inc. | Velocity detection for posture-responsive therapy |
US9737719B2 (en) | 2012-04-26 | 2017-08-22 | Medtronic, Inc. | Adjustment of therapy based on acceleration |
US9180242B2 (en) | 2012-05-17 | 2015-11-10 | Tandem Diabetes Care, Inc. | Methods and devices for multiple fluid transfer |
US9555186B2 (en) | 2012-06-05 | 2017-01-31 | Tandem Diabetes Care, Inc. | Infusion pump system with disposable cartridge having pressure venting and pressure feedback |
EP2879758B1 (en) | 2012-08-01 | 2018-04-18 | Pacesetter, Inc. | Biostimulator circuit with flying cell |
US8761717B1 (en) | 2012-08-07 | 2014-06-24 | Brian K. Buchheit | Safety feature to disable an electronic device when a wireless implantable medical device (IMD) is proximate |
US9395234B2 (en) | 2012-12-05 | 2016-07-19 | Cardiocom, Llc | Stabilizing base for scale |
US9173998B2 (en) | 2013-03-14 | 2015-11-03 | Tandem Diabetes Care, Inc. | System and method for detecting occlusions in an infusion pump |
FR3006575B1 (en) * | 2013-06-05 | 2018-02-02 | L.3 Medical | DEVICE FOR REMOTELY TRACKING AT LEAST ONE MEDICAL DEVICE |
US9830424B2 (en) | 2013-09-18 | 2017-11-28 | Hill-Rom Services, Inc. | Bed/room/patient association systems and methods |
US20150277397A1 (en) * | 2014-03-31 | 2015-10-01 | Elwha LLC, a limited liability company of the State of Delaware | Quantified-Self Machines and Circuits Reflexively Related to Food Fabricator Machines and Circuits |
US10318123B2 (en) | 2014-03-31 | 2019-06-11 | Elwha Llc | Quantified-self machines, circuits and interfaces reflexively related to food fabricator machines and circuits |
US9922307B2 (en) | 2014-03-31 | 2018-03-20 | Elwha Llc | Quantified-self machines, circuits and interfaces reflexively related to food |
US10127361B2 (en) | 2014-03-31 | 2018-11-13 | Elwha Llc | Quantified-self machines and circuits reflexively related to kiosk systems and associated food-and-nutrition machines and circuits |
US10058708B2 (en) | 2015-06-30 | 2018-08-28 | Cardiac Pacemakers, Inc. | Heart failure event detection using minimum heart rate |
CN108697571B (en) | 2015-10-09 | 2021-07-13 | Kpr美国有限责任公司 | Compression garment compliance |
US10610132B2 (en) | 2016-08-02 | 2020-04-07 | Medtronic, Inc. | Step detection using accelerometer axis |
US10952686B2 (en) | 2016-08-02 | 2021-03-23 | Medtronic, Inc. | Mobile application to prompt physical action to measure physiologic response in implantable device |
CN106551691B (en) * | 2016-12-02 | 2020-01-21 | 清华大学 | Heart rate variability analysis method, device and application |
US11596795B2 (en) | 2017-07-31 | 2023-03-07 | Medtronic, Inc. | Therapeutic electrical stimulation therapy for patient gait freeze |
US11894139B1 (en) | 2018-12-03 | 2024-02-06 | Patientslikeme Llc | Disease spectrum classification |
US11911325B2 (en) | 2019-02-26 | 2024-02-27 | Hill-Rom Services, Inc. | Bed interface for manual location |
US10504496B1 (en) | 2019-04-23 | 2019-12-10 | Sensoplex, Inc. | Music tempo adjustment apparatus and method based on gait analysis |
US11642035B2 (en) | 2019-06-28 | 2023-05-09 | Medtronic, Inc. | Heart rate recovery assessment |
US11717186B2 (en) | 2019-08-27 | 2023-08-08 | Medtronic, Inc. | Body stability measurement |
US11602313B2 (en) | 2020-07-28 | 2023-03-14 | Medtronic, Inc. | Determining a fall risk responsive to detecting body position movements |
US20220071513A1 (en) * | 2020-09-08 | 2022-03-10 | Medtronic, Inc. | Detection of changes in patient health based on peak and non-peak patient activity data |
Family Cites Families (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4086916A (en) | 1975-09-19 | 1978-05-02 | Joseph J. Cayre | Cardiac monitor wristwatch |
US4112936A (en) * | 1976-09-27 | 1978-09-12 | Blachly Paul H | Bite block assembly adapted for adjustable mounting and holding of oral airways and method of using same |
US4353375A (en) * | 1977-04-26 | 1982-10-12 | The United States Of America As Represented By The Department Of Health & Human Services | Activity monitor for ambulatory subjects |
US4731051A (en) * | 1979-04-27 | 1988-03-15 | The Johns Hopkins University | Programmable control means for providing safe and controlled medication infusion |
US4275727A (en) * | 1980-01-07 | 1981-06-30 | Keeri Szanto Michael | Device for monitoring and controlling self-administered intravenous drug dosage |
US5101814A (en) * | 1989-08-11 | 1992-04-07 | Palti Yoram Prof | System for monitoring and controlling blood glucose |
US5012814A (en) | 1989-11-09 | 1991-05-07 | Instromedix, Inc. | Implantable-defibrillator pulse detection-triggered ECG monitoring method and apparatus |
US5086772A (en) | 1990-07-30 | 1992-02-11 | Telectronics Pacing Systems, Inc. | Arrhythmia control system employing arrhythmia recognition algorithm |
US5113869A (en) | 1990-08-21 | 1992-05-19 | Telectronics Pacing Systems, Inc. | Implantable ambulatory electrocardiogram monitor |
US5293879A (en) * | 1991-09-23 | 1994-03-15 | Vitatron Medical, B.V. | System an method for detecting tremors such as those which result from parkinson's disease |
US5313953A (en) | 1992-01-14 | 1994-05-24 | Incontrol, Inc. | Implantable cardiac patient monitor |
US5312446A (en) * | 1992-08-26 | 1994-05-17 | Medtronic, Inc. | Compressed storage of data in cardiac pacemakers |
US5404877A (en) | 1993-06-04 | 1995-04-11 | Telectronics Pacing Systems, Inc. | Leadless implantable sensor assembly and a cardiac emergency warning alarm |
US5404887A (en) | 1993-11-04 | 1995-04-11 | Scimed Life Systems, Inc. | Guide wire having an unsmooth exterior surface |
US5411031A (en) | 1993-11-24 | 1995-05-02 | Incontrol, Inc. | Implantable cardiac patient monitor |
US5520637A (en) * | 1995-01-31 | 1996-05-28 | Pager; David | Closed-loop system for infusing oxytocin |
SE9504707L (en) * | 1995-12-29 | 1997-06-30 | Alfa Laval Agri Ab | activity Measurement |
-
1998
- 1998-05-13 US US09/078,221 patent/US6045513A/en not_active Expired - Lifetime
-
1999
- 1999-05-11 CA CA002331500A patent/CA2331500A1/en not_active Abandoned
- 1999-05-11 JP JP2000547910A patent/JP2002514454A/en active Pending
- 1999-05-11 WO PCT/US1999/010282 patent/WO1999058056A1/en active Application Filing
- 1999-05-11 EP EP99924176A patent/EP1079733B1/en not_active Expired - Lifetime
- 1999-05-11 DE DE69941356T patent/DE69941356D1/en not_active Expired - Lifetime
- 1999-09-28 US US09/408,469 patent/US6280409B1/en not_active Expired - Lifetime
-
2000
- 2000-01-19 US US09/487,561 patent/US6102874A/en not_active Expired - Lifetime
Also Published As
Publication number | Publication date |
---|---|
WO1999058056A1 (en) | 1999-11-18 |
DE69941356D1 (en) | 2009-10-15 |
US6280409B1 (en) | 2001-08-28 |
US6102874A (en) | 2000-08-15 |
EP1079733B1 (en) | 2009-09-02 |
US6045513A (en) | 2000-04-04 |
JP2002514454A (en) | 2002-05-21 |
EP1079733A1 (en) | 2001-03-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6045513A (en) | Implantable medical device for tracking patient functional status | |
US6190324B1 (en) | Implantable medical device for tracking patient cardiac status | |
EP2654560B1 (en) | Heart failure detection with a sequential classifier | |
US20200281522A1 (en) | System and method for improved obstructive sleep apnea diagnostic for implantable devices | |
US8660638B2 (en) | Syncope logbook and method of using same | |
US9232900B2 (en) | System and method for analyzing a patient status for congestive heart failure for use in automated patient care | |
US8951203B2 (en) | Measures of cardiac contractility variability during ischemia | |
US7725186B1 (en) | Complimentary activity sensor network for disease monitoring and therapy modulation in an implantable device | |
US7676262B1 (en) | Methods and devices for determining exercise compliance diagnostics | |
US20090287103A1 (en) | Systems and methods for monitoring patient activity and/or exercise and displaying information about the same | |
US11633614B2 (en) | Wearable cardiac device to monitor physiological response to activity | |
EP1533741A2 (en) | Collection and analysis of procedural information | |
WO2023141402A1 (en) | Systems and methods for performing exertion testing of a patient wearing an ambulatory medical device | |
Burkhardt | An implementation of ambulatory, wire-free single-lead electrocardiograph telemetry |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
EEER | Examination request | ||
FZDE | Discontinued |