WO2011080191A1

WO2011080191A1 - Monitoring blood pressure

Info

Publication number: WO2011080191A1
Application number: PCT/EP2010/070557
Authority: WO
Inventors: Bo Olde; Kristian Solem
Original assignee: Gambro Lundia Ab
Priority date: 2009-12-28
Filing date: 2010-12-22
Publication date: 2011-07-07

Abstract

A device (25) is configured to determine a blood pressure value of a subject, based on a combination of signals obtained from sensors (35, 40) associated at least with an extracorporeal blood flow circuit (20) that is connected in fluid communication with the vascular system of the subject. The combination of signals may be a first signal from a first pulse sensor (40) arranged to detect pressure waves in the extracorporeal blood flow circuit (20) and a second signal indicative of an activation of a pulse generator (30) associated with the patient. Alternatively the combination of signals may be a first signal from a first pulse sensor arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator (3) associated with the extracorporeal blood flow circuit (20). The device comprises a signal processor which is configured to process the first and second signals to determine a time difference between a first time point associated with the first signal and a second time point associated with the second signal; and calculate the blood pressure value based on the time difference. The device (25) may be included in an apparatus (200) for blood treatment, such as a dialysis machine.

Description

MONITORING BLOOD PRESSURE

Technical field

The present invention generally relates to monitoring of blood pressure, or a related property, in a human or animal subject, in particular when the vascular system of the subject is connected in fluid communication with an extracorporeal fluid system. The present invention is e.g. applicable in arrangements for extracorporeal blood treatment.

Background Art

The prior art comprises US2005/0010118 which discloses a method and device for measuring the pulse rate and blood pressure of a patient connected to a dialysis machine, by subjecting a pressure signal to a Fourier (FFT) analysis for identifying a frequency component of the pressure wave caused by the patient's heartbeat. The intensity of the frequency component is alleged to indicate of the patient's blood pressure. However, it known e.g. from US6623443, that the amplitude of the heart component in the pressure signal represents the pressure in the fistula. Thus, US2005/0010118 presumes that the flow resistance in the fistula is constant, and that the blood flow through the fistula correlates with the patient's blood pressure. However, the blood pressure is known to be controlled independently of the local blood flow, which is controlled in dependence of the local need for oxygen and nutrition. Thus, the blood flow through the artery connected to the fistula may change without the blood pressure being changed. If the blood flow through the artery changes, so does the blood flow through the fistula. Thus, the method proposed in

US2005/0010118 is not generally applicable for measuring a patient's blood pressure.

US2005/0261594 discloses an ambulatory blood pressure monitor in the form of an adhesive patch sensor. The patch sensor includes a combination of a pulse oximeter and a horseshoe-shaped metal electrode and is designed to be attached to the head of a patient. The electrode generates an electrical waveform which represents the patient' s heartbeat, whereas the pulse oximeter generates an optical waveform which represents the patient's heartbeat. A monitoring device records a difference in propagation time between the electrical waveform and the optical waveform, and determines a blood pressure value of the patient based on the difference in propagation time. Pulse oximeters are known to be sensitive to disturbances, such as patient movement, and it can therefore be envisioned that the blood pressure monitor is less suitable for continuous monitoring of blood pressure.

The prior art also comprises EP0829227, US4907596, US5743857, US6736789, US2002/0193691, and US2009/0050544.

Summary It is an object of the invention to at least partly overcome one or more limitations of the prior art. Specifically, it is an object to provide an alternative or complementary technique for monitoring the blood pressure of a patient connected to an extracorporeal blood circuit.

This and other objects, which will appear from the description below, are at least partly achieved by means of devices, an apparatus for blood treatment, a method, and a computer program product according to the independent claims, embodiments thereof being defined by the dependent claims.

A first aspect of the invention is a device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit is connected in fluid communication with the vascular system of the subject, wherein the device comprises an input configured to obtain a combination of signals, wherein the combination of signals is either of: i) a first signal from a first pulse sensor arranged to detect pressure waves in the extracorporeal blood flow circuit and a second signal indicative of an activation of a pulse generator associated with the subject; and ii) a first signal from a first pulse sensor arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator associated with the extracorporeal blood flow circuit; wherein the device further comprises a signal processor configured to: process the first and second signals to determine a time difference between a first time point associated with the first signal and a second time point associated with the second signal; and calculate the blood pressure value based on the time difference.

A second aspect of the invention is a device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit is connected in fluid

communication with the vascular system of the subject, wherein the device comprises input means for obtaining a combination of signals, wherein the combination of signals is either of: i) a first signal from a first pulse sensor arranged to detect pressure waves in the extracorporeal blood flow circuit and a second signal indicative of an activation of a pulse generator associated with the subject; and ii) a first signal from a first pulse sensor arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator associated with the extracorporeal blood flow circuit; wherein the device further comprises: means for processing the first and second signals to determine a time difference between a first time point associated with the first signal and a second time point associated with the second signal; and means for calculating the blood pressure value based on the time difference.

A third aspect of the invention is an apparatus for blood treatment, comprising an extracorporeal blood flow circuit adapted for connection to the vascular system of a subject and operable to circulate blood from the subject through a blood processing device and back to the subject, and the device according to the first or second aspects. A fourth aspect of the invention is a method for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit is connected in fluid

communication with the vascular system of the subject, wherein the method comprises the step of obtaining a combination of signals, wherein the combination of signals is either of: i) a first signal from a first pulse sensor arranged to detect pressure waves in the extracorporeal blood flow circuit and a second signal indicative of an activation of a pulse generator associated with the subject; and ii) a first signal from a first pulse sensor arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator associated with the extracorporeal blood flow circuit; wherein the method further comprises the step of: processing the first and second signals to determine a time difference between a first time point associated with the first signal and a second time point associated with the second signal; and calculating the blood pressure value based on the time difference.

A fifth aspect of the invention is a computer program product comprising

instructions for causing a computer to perform the method of the fourth aspect.

Still other objectives, features, aspects and advantages of the present invention will appear from the following detailed description, from the attached claims as well as from the drawings.

Brief Description of the Drawings

Exemplary embodiments of the invention are described in more detail with reference to the accompanying schematic drawings.

Fig. 1 is a schematic view of a system for hemodialysis treatment including an extracorporeal blood flow circuit.

Fig. 2 is a block diagram of a system configuration for use in a first monitoring concept.

Fig. 3 is a flow chart of a process for monitoring blood pressure in a patient.

Fig. 4 is a plot of a pulse generation signal and a pulse wave signal.

Fig. 5 is a block diagram of a system configuration for use in a second monitoring concept.

Fig. 6 is a block diagram of a system configuration for use in a third monitoring concept.

Fig. 7 is a view of a cuff used for data collection.

Fig. 8 is a block diagram of a surveillance device for monitoring blood pressure.

Fig. 9(a) is a plot in the time domain of a pressure signal containing both pump pulses and heart pulses, and Fig. 9(b) is a plot of the corresponding signal in the frequency domain.

Fig. 10 is a flow chart of a process for signal analysis of a pressure signal obtained in the system configuration of Fig. 1. Fig. 11 is a plot to illustrate an extrapolation process for generating a predicted signal profile.

Fig. 12 is a flow chart of a process for obtaining a predicted signal profile.

Fig. 13(a) is a plot to illustrate an interpolation process for generating a predicted signal profile, and Fig. 13(b) is an enlarged view of Fig. 13(a).

Fig. 14(a) represents a frequency spectrum of pump pulses at one flow rate, Fig. 14(b) represents corresponding frequency spectra for three different flow rates, wherein each frequency spectrum is given in logarithmic scale and mapped to harmonic numbers, Fig. 14(c) is a plot of the data in Fig. 14(b) in linear scale, and Fig 14(d) is a phase angle spectrum corresponding to the frequency spectrum in Fig. 14(a).

Fig. 15 is a schematic view of an adaptive filter structure operable to filter a pressure signal based on a predicted signal profile.

Fig. 16 is a plot of myoelectric signals recorded on a patient. Detailed Description of Exemplary Embodiments

In the following, embodiments will be described with reference to an extracorporeal blood flow circuit. In particular, exemplary embodiments for monitoring the blood pressure of a patient connected to such a circuit are described, based on three monitoring concepts. A description is also given of various physiological phenomena that may be detected in such monitoring, as well as embodiments for detecting and extracting signals indicative of such physiological phenomena. Throughout the following description, like elements are designated by the same reference signs.

I. EXAMPLE OF EXTRACORPOREAL CIRCUIT

Fig. 1 shows an example of an extracorporeal blood flow circuit 20, which is part of an apparatus for blood treatment , in this case a dialysis machine. The extracorporeal blood flow circuit 20 comprises components 1-14 to be described in the following. Thus, the extracorporeal blood flow circuit 20 comprises an access device for blood extraction in the form of an arterial needle 1, and an arterial tube segment 2 which connects the arterial needle 1 to a blood pump 3 which may be of peristaltic type, as indicated in Fig. 1. At the inlet of the pump there is a pressure sensor 4a (hereafter referred to as arterial sensor) which measures the pressure before the pump in the arterial tube segment 2. The blood pump 3 forces the blood, via a tube segment 5, to the blood- side of a dialyser 6. Many dialysis machines are additionally provided with a pressure sensor 4b that measures the pressure between the blood pump 3 and the dialyser 6. The blood is lead via a tube segment 10 from the blood-side of the dialyser 6 to a venous drip chamber or deaeration chamber 11 and from there back to the patient via a venous tube segment 12 and an access device for blood reintroduction in the form of a venous needle 14. A pressure sensor 4c (hereafter referred to as venous sensor) is provided to measure the pressure on the venous side of the dialyser 6. In the illustrated example, the pressure sensor 4c measures the pressure in the venous drip chamber 11. Both the arterial needle 1 and the venous needle 14 are connected to the vascular system of a human or animal patient by means of a blood vessel access. The blood vessel access may be of any suitable type, e.g. a fistula, a Scribner- shunt, a graft, etc. Depending on the type of blood vessel access, other types of access devices may be used instead of needles, e.g. catheters.

Herein, the "venous side" of the extracorporeal circuit 20 refers to the part of the blood path located downstream of the blood pump 3, whereas the "arterial side" of the extracorporeal circuit 20 refers to the part of the blood path located upstream of the blood pump 3. In the example of Fig. 1, the venous side is made up of tube segment 5, the blood- side of the dialyser 6, tube segment 10, drip chamber 11 and tube segment 12a, and the arterial side is made up of tube segment 2b.

The dialysis machine also includes a dialysis fluid circuit 20', which is only partly shown in Fig. 1 and which is operated to prepare, condition and circulate dialysis fluid through the dialysis fluid-side of the dialyser 6, via tube segments 15, 16.

In Fig. 1, a control unit 23 is provided, inter alia, to control the blood flow in the circuit 20 by controlling the revolution speed of the blood pump 3.

A surveillance/monitoring device 25 is configured to monitor the blood pressure of the patient. In the example of Fig. 1, the surveillance device 25 is electrically connected to receive measurement data (also denoted "pulse wave signal" in the following) from one or more of the pressure sensors 4a-4c. The surveillance device is also connected to receive an output signal (also denoted "pulse generation signal" in the following) from a pulse generation sensor 35, the function of which will be described in Section II below.

As indicated in Fig. 1, the device 25 may also be connected to the control unit 23. Alternatively or additionally, the device 25 may be connected to a pump sensor 26, such as a rotary encoder (e.g. conductive, optical or magnetic) or the like, for indicating the frequency and/or phase of the blood pump 3. The device 25 is tethered or wirelessly connected to a local or remote device 27 for generating an audible/visual/tactile alarm or warning signal based on the calculated blood pressure values (or a diagnose deduced based on the calculated values), for displaying the calculated values, and/or for storing the blood pressure values calculated by the device 25. The surveillance device 25 and/or the alarm/display/storage device 27 may alternatively be incorporated as part of the dialysis machine, or be separate components.

It is to be understood that the surveillance device 25 may execute any number of other functions, such as verifying proper operation of the extracorporeal system 20 or the connection between the extracorporeal system 20 and the vascular system. For example, the device 25 may be arranged to detect if any one of the access devices 1, 14 is dislodged from the blood vessel access. In the example of Fig. 1, the surveillance device 25 comprises a data acquisition part 28 for sampling a time sequence of data from the pressure sensor(s) 4a-4c and the pulse generation sensor 35 and, optionally, for pre-processing the sampled data. For example the data acquisition part 28 may include an A/D converter with a required minimum sampling rate and resolution, one or more signal amplifiers, one or more filters to remove undesired signal components in the sampled data, such as offset, high frequency noise and supply voltage disturbances. Each data sample from the pressure sensor may represent an instantaneous pressure of the blood in the circuit at the location of the pressure sensor 4a- 4c. Each data sample from the pulse generation sensor 35 may e.g. represent a

current/voltage generated by the patient's body, or an instantaneous pressure of the blood in the vascular system of the patient at the location of the sensor 35. The pre-processing in the data acquisition part 28 results in input data for a data analysis part 29 that executes the actual monitoring process. Depending on implementation, the surveillance device 25 may use digital components or analog components, or a combination thereof, for acquiring, processing and analysing data.

II. MONITORING PATIENT BLOOD PRESSURE AND ARTERIAL STATUS

Embodiments of the invention relates to monitoring the blood pressure of a patient that is connected to an extracorporeal blood circuit. The extracorporeal circuit is connected to the vascular system of the patient so as to circulate blood from the patient through a blood processing device and back to the patient. The blood pressure is monitored by determining a time difference between two measurement signals originating from two different sensors that both detect the operation/activity of a pulse generator associated with the patient or the extracorporeal circuit. At least one of the sensors (denoted "pulse wave sensor" in the following) is arranged to detect a pulse wave that originates from the pulse generator and propagates through part of the vascular system before reaching the sensor. The blood pressure in the vascular system affects the propagation speed of the pulse wave, with a higher blood pressure causing an increased propagation speed.

As discussed and exemplified below, the other sensor (denoted "pulse generation sensor" in the following) may also be arranged to detect the pulse wave after it has propagated through part of the vascular system. As long as the pulse wave propagates different distances in the vascular system on its way to the pulse wave sensor and the pulse generation sensor, respectively, it is possible to determine a time difference between the detection times at the pulse wave sensor and the pulse generation sensor, which time difference is representative of the instant blood pressure in the vascular system.

Alternatively, the pulse generation sensor may be configured to detect the

operation/activity of the pulse generator on a non- vascular signal path and/or via signals that have a different propagation speed than the pulse waves. In such a variant, the blood pressure affects the detection time at the pulse wave sensor but not at the pulse generation sensor, allowing the observed time difference to be attributed to the blood pressure in the vascular system of the patient.

In this context, it should be noted that the propagation speed of the pulse wave ("pulse wave velocity") in the vascular system is a function of not only the blood pressure in the vascular system, but also of the mechanical properties of the arteries (denoted "arterial status" in the following). The pulse wave velocity may also be influenced by further properties associated with the vascular system. In embodiments of the invention, the above-mentioned time difference is obtained continuously or intermittently during a blood treatment session, and it may be assumed that all properties except blood pressure are essentially invariant during such a treatment session. Thus, variations in the time difference observed during a blood treatment session may be attributed to variations in blood pressure.

However, in other embodiments of the invention, the time difference is obtained over a sequence of different blood treatment sessions for one and the same patient. The resulting time differences may be processed to calculate an average time difference for each treatment session. If the average blood pressure of the patient is known, e.g. via a conventional blood pressure measurement or by averaging blood pressure values obtained via the aforesaid time differences during each session, it is possible to identify a deviation between the change in average time difference and the change in average blood pressure over a sequence of treatment sessions. Such a deviation may be attributed to a change in the arterial status of the patient, e.g. the arterial stiffness. Alternatively, the deviation may be obtained by assuming that the average blood pressure of the patient is essentially constant between treatment sessions, and attributing any change in the average time difference to a change in the arterial status of the patient.

Arterial stiffness increases as a consequence of age, vascular calcification and artherosclerosis, and it is known that e.g. myocardial infraction, stroke and peripheral vascular disease are a direct consequence of artherosclerosis. Prevention of these and other cardiovascular events may be achieved with the early detection of artherosclerosis. Thus, providing a technique for monitoring the arterial status of the patient has the potential of facilitating/improving cardiovascular risk stratification.

In certain embodiments of the invention, the arterial status may be determined in combination with the blood pressure value, whereas in other embodiments only the arterial status is determined based on the time differences.

Below, embodiments of the invention will be further explained and exemplified in relation to three monitoring concepts, each using a specific combination of pulse generator, pulse generation sensor and pulse wave sensor for monitoring of the blood pressure and/or arterial status of a patient connected to an extracorporeal circuit.

First monitoring concept Fig. 2 illustrates a combination of a pulse generator 30, a pulse generation sensor 35 and a pulse wave sensor 40 according to the first monitoring concept. In the example of Fig. 2, the pulse generator 30 is a physiological phenomenon in the patient's body, such as the heart or the breathing system. The pulse generation sensor 35 is attached to the patient's body, and the pulse wave sensor 40 is associated with the extracorporeal circuit 20. For example, the pulse wave sensor 40 may be one of the existing pressure sensors 4a- 4c (see Fig. 1) in the circuit 20, or a dedicated sensor attached to the circuit 20. In Fig. 2, the extracorporeal circuit 20 is illustrated as part of a dialysis machine 200, which also includes a dialysis fluid flow circuit 20' . A surveillance device 25 is included in or attached to the dialysis machine 200 to, inter alia, monitor the blood pressure of the patient.

Fig. 3 is a flow chart of an embodiment of a method according to the first monitoring concept, which is carried out by the surveillance device 25 based on detection of one or more pulse waves generated by the physiological phenomenon 30 in the patient. The (or each) pulse wave is detected by the pulse wave sensor 40 in the extracorporeal circuit 20 connected to, and in fluid communication with, the vascular system of the patient via one or more access devices 1, 14. In the illustrated example, the method iteratively executes a sequence of steps 301-306, with each sequence resulting in a blood pressure value.

In step 301 a pulse wave signal is acquired from pulse wave sensor 40, and in step 302 a pulse generation signal is acquired from the pulse generation sensor 35. In step 303, the pulse wave signal is processed for identification of a pressure pulse that originates from the activation of the physiological phenomenon 30 in the patient. The processing in step 303 extracts an arrival time point of the thus-identified pressure pulse. In step 304, the pulse generation signal is processed for extraction of a reference time point which also corresponds to the activation of the physiological phenomenon 30 in the patient. It is to be understood that the arrival and reference time points are given in a common time frame, such that a time difference may be calculated based on these time points. In step 305, this time difference is calculated and used for generating a relative or absolute value of the blood pressure in the patient's vascular system. Finally, in step 306 the blood pressure value is output and the procedure returns to step 301.

It should be understood that the sequence and ordering of steps in Fig. 3 is merely given as an example. Further, the retrieval and processing of the pulse generation signal (steps 302 and 304) may be substituted for a step of receiving the reference time point, which is calculated by a processing device connected to or included in the pulse generation sensor 35. Similarly, the retrieval and processing of the pulse wave signal (steps 301 and 303) may be substituted for a step of receiving the arrival time point, which is calculated by a processing device connected to or included in the pulse wave sensor 40.

Embodiments of the first monitoring concept utilize the fact that physiological phenomena arising in the patient's body cause pressure waves in the blood streams of the patient. It has been found that these pressure waves are, in turn, conducted via the blood vessel access and the blood line/tubing in the circuit 20 to the pulse wave sensor 40 (e.g. one of the pressure sensors/transducers 4a-4c). By signal analysis, it is thus possible to extract pressure pulses from a specific physiological phenomenon in a pulse wave signal obtained from the pulse wave sensor 40.

In certain embodiments, the monitoring utilizes the combined approach of measuring the pressure in the extracorporeal circuit 20 and concurrently measuring an electrical current generated by muscle activity in the patient associated with the physiological phenomenon 30. The electrical current may travel by the propagation speed of

electromagnetic waves through the vascular system, and thus the electrical current (or voltage) measurement may provide an instant temporal representation of a physiological pulse that originates from the physiological phenomenon 30. On the other hand, the pulse wave caused by the physiological pulse travels through the vascular system of the patient at a propagation speed (pulse wave velocity) of about 3-20 m/s. Thus, the pressure measurement provides a delayed temporal representation of the same physiological pulse. Since the pulse wave velocity is a function, inter alia, of the blood pressure in the vascular system, the time difference between the pulses in the current/voltage and pressure measurements is thus a function of the patient's blood pressure.

In the first monitoring concept, the pulse wave is detected by a pulse wave sensor 40 in the extracorporeal circuit 40. Thus, the pulse wave propagates from its origin in the patient through part of the vascular system, across the fluid connection formed by the blood vessel access and the access devices 1, 14 and through part of the extracorporeal circuit 20 to the pulse wave sensor 40. The total propagation time of the pulse wave from its origin to the pulse wave sensor 40 is thus made up a VS transfer time (propagation time through the vascular system) and an EC transfer time (propagation time through the extracorporeal circuit 20). The VS transfer time is a function of the properties (including blood pressure/arterial status) of the vascular system, whereas the EC transfer time is a function of the properties of the propagation path in the extracorporeal circuit 20. Thus, a corrected time difference may be obtained by subtracting an EC transfer time value from the time difference determined in step 305.

In certain embodiments, the EC transfer time may be significantly shorter than the VS transfer time. Thus, the EC transfer time value may be neglected, and the time difference may be taken to directly reflect the blood pressure. Alternatively, the EC transfer time value may be approximated by a fixed and predefined value. In another alternative, the EC transfer time value is estimated based on the actual pressure (absolute, relative, or average) in the propagation path through the extracorporeal circuit 20, wherein the actual pressure may be derived from any suitable sensor in the extracorporeal circuit (including the pressure sensors 4a-4c). The transfer time decreases if the actual pressure increases, i.e., high pressure equals short transfer time. The transfer time value may be calculated based on, e.g., a physical model or a look-up table. The model/table may not only include information about pressure (absolute, relative, or average), but also information about material (elasticity, plasticity, etc), geometry (length, diameter, wall thickness, etc), temperature (blood and ambient), mechanical factors (clamp, tension, actuators, kinking/occlusion, etc), blood properties (viscosity, chemical composition, etc), etc.

In certain embodiments, the time difference obtained in step 305 may also include a delay between the instant pulse in the pulse generation signal and the actual activation of the physiological phenomenon 30. For example, if the pulse generation signal is an electrical current generated by the activity of the patient's heart, there may be a so-called pre-ejection period (PEP) between the onset of the instant pulse and the actual cardiac ejection that generates the pulse wave. The length of the PEP may be measured for the patient and used to determine (by subtraction) a corrected time difference in step 305.

In other embodiments of the first monitoring concept, the pulse generation signal does not provide an instant representation of the physiological pulse, but may be obtained with a certain time delay to the generation of the physiological pulse. For example, if the physiological phenomenon 30 is a heart, the pulse generation sensor 35 may be, e.g., a pressure sensor or a photoplethysmo graph (PPG) such as a pulse oximeter. Irrespective of implementation, the pulse generation sensor 35 is arranged in contact with or in proximity to the patient's body to detect the pressure pulse originating from the activity of the heart (i.e. a heartbeat).

Further examples of pulse generation sensors 35 for detection of electrical or nonelectrical signals on the patient are given in Section III below.

As noted above, the pulse wave sensor 40 may be any one of the existing pressure sensors 4a-4c in the extracorporeal circuit 20. Alternatively, the pulse wave sensor 40 may be a dedicated sensor that is arranged in contact with or in proximity to the extracorporeal circuit 20 to detect the pulse wave. Such a dedicated pulse wave sensor may be of any suitable kind, including a pressure sensor, a PPG sensor, other types of optical sensors, ultrasound sensors (e.g. Doppler), electromagnetic sensors, etc.

Embodiments of the first monitoring concept enable continuous and automatic monitoring of the patient's blood pressure. Furthermore, the monitoring is enabled whenever the vascular system of the patient is connected to the extracorporeal circuit 20, such that pulse waves originating from the relevant physiological phenomenon 30 are detectable by means of the pulse wave sensor 40 in the extracorporeal circuit 20. For example, monitoring may be carried out during blood treatment.

Embodiments of the first monitoring concept may thus facilitate the procedure of automatic monitoring of the patient's blood pressure, since it obviates the need to attach plural pulse-detection devices to the patient for the purpose of monitoring blood pressure. Furthermore, the pulse wave sensor 40 detects the pulse wave in the extracorporeal circuit 40, and is thereby relatively insensitive to patient movement.

Embodiments of the first monitoring concept also enable post-treatment evaluation of the patient's blood pressure based on signals recorded during a blood treatment session.

To further exemplify the first monitoring concept, Fig. 4 shows a pulse generation signal Rl obtained from an electrocardiograph (ECG) device attached to a patient, and a monitoring signal PI obtained by processing a pressure signal acquired from the arterial pressure sensor 4a in the extracorporeal circuit 20 in Fig. 1. Both the pulse generation signal Rl and the monitoring signal PI originate from heartbeats in the patient. Following a heartbeat, a corresponding electrical impulse travels essentially instantaneously from the patient' s heart to one or more skin electrodes of the ECG device, which outputs the pulse generation signal Rl to the surveillance device 25. At a later time, a pulse wave induced by the same heartbeat propagates through the patient's arteries and arrives at the pressure sensor 4a, which generates pressure data which is acquired and processed by the surveillance device 25. Thus, based on the pulse generation signal Rl and the monitoring signal PI, the surveillance device 25 is capable of identifying the respective pulse and calculating the time difference ΔΤ between the pulses.

The time difference ΔΤ may be calculated by identifying corresponding features in the pulses, and taking the time difference between these features. Such features include the peak amplitude, the leading edge or the trailing edge of each pulse. The need for mapping of features between pulses depends on the temporal extent of the pulses in relation to the time difference ΔΤ, as well as the required precision of the blood pressure value. For example, a smaller time difference may call for a more precise mapping of features to attain given precision.

An alternative way of calculating the time difference ΔΤ involves cross-correlating a pulse segment in the pulse wave signal (e.g. PI) with a pulse segment in the pulse generation signal (e.g. Rl), wherein the location of maximum correlation value will correspond to the time difference ΔΤ. In order to suppress noise and other signal interferences, both pulse segments may be selected to contain a plurality of pulses, whereby the maximum correlation value will indicate the average time difference between pulses in the pulse segments. In a variant, one of the pulse segments is first processed to generate a synthetic pulse segment that contains a sequence of synthetic pulses with identical signal profiles, wherein the signal profile of the synthetic pulse corresponds to a known/predefined/predicted signal profile of pulses in the other pulse segment. The synthetic pulse segment is generated by identifying the time points of the relevant pulses in the pulse segment, and by arranging the synthetic pulses with a mutual timing that matches the identified time points. The synthetic pulse segment is then cross-correlated with the other pulse segment. This variant may serve to increase the SNR of the correlation values, and thus the accuracy of the time difference ΔΤ. The cross-correlation may be substituted for any equivalent convolution technique.

Based on the time difference ΔΤ, the surveillance device estimates the patient's current blood pressure value. The current blood pressure value may be given on a relative scale with respect to a preceding blood pressure value, allowing changes in the patient's blood pressure to be monitored over time.

Alternatively, the time difference may be converted into blood pressure on an absolute scale. Generally, the blood pressure (BP) may be approximated to be a linear function of l/ΔΤ: BP = a + β/ΔΤ, assuming that a and β are constant during the blood treatment session, and possibly between blood treatment sessions, at least for the individual patient. However, as noted above, the proportionality constant β may change over a sequence of treatment sessions, e.g. due to changes in the arterial status of the patient. The constants a and β, and thus the linear function, may be determined by obtaining at least two absolute blood pressure values from a calibration device during monitoring, such that at least two time differences may be associated with absolute blood pressure values.

Alternatively, by assuming that β is essentially constant between two treatment sessions, it is possible to calibrate the time differences by associating a single time difference value with an absolute blood pressure value from the calibration device. The calibration device, which may be manually or automatically controlled (e.g. by the surveillance device 25), may be based on any type of conventional technique for measuring absolute blood pressure. For example, the calibration device may comprise an inflatable cuff which is attached to the patient's arm to restrict blood flow, as is well-known in the art.

In a variation, each current blood pressure value (relative or absolute) is estimated by combining a number of time differences calculated during the current and previous iterations of the monitoring procedure (cf. Fig. 3), e.g. by calculating the average of these time differences.

It is realized that by identifying a sequence of pulses in the pulse wave signal PI and the pulse generation signal Rl, and by pairing pulses between the signals PI, Rl and calculating the time difference between the pairs of pulses, the surveillance device 25 is capable of continuously monitoring the patient's blood pressure. The result may be presented, e.g. displayed, to medical staff and may be useful to detect, track or predict disorders and possibly take a corrective action. The surveillance device 25 may also be configured to identify one or more alarm conditions, e.g. if a given number (or fraction) of the latest blood pressure values fall outside a given blood pressure range, or go

above/below a given threshold value, or if the absolute rate of change in blood pressure exceeds a given threshold value. Upon identification of an alarm condition, the

surveillance device 25 may cause an alarm device (e.g. 27 in Fig. 1) to issue an alarm or warning signal, and/or alert a control unit (e.g. 23 in Fig. 1) to take appropriate action. Such action may involve one or more of performing a calibration of the absolute blood pressure values, changing a parameter of the blood treatment process (e.g. the

ultrafiltration rate (UFR), the salt concentration in the dialysis fluid, the temperature of the dialysis fluid, etc). Second monitoring concept

Fig. 5 illustrates a combination of a pulse generator 30, a pulse generation sensor 35 and a pulse wave sensor 40 according to the second monitoring concept. In the example of Fig. 5, the pulse generator 30 is a separate electromechanical device which is arranged in proximity of or contact with the patient. The pulse generation sensor 35 is arranged in contact with or in proximity to the patient's body, and the pulse wave sensor 40 is associated with the extracorporeal circuit 20, as in the first monitoring concept (Fig. 2).

All embodiments discussed above in relation to the first monitoring concept are equally applicable to the second monitoring concept, subject to the following

modifications.

One difference compared to the first monitoring concept is that the pulse generator

30 is not a physiological phenomenon in the patient but an electromechanical device which is operable to mechanically generate pulse waves in the vascular system of the patient. One advantage of such a device 30 is that the magnitude and/or the phase and/or the rate of the generated pulse waves may be optimised/controlled to facilitate the detection of the pressure pulse in the pulse wave signal. Likewise, the placement of electromechanical device 30 on the patient's body may be selected to facilitate the detection of the pulse waves.

In one embodiment, the electromechanical device 30 is operated to generate pulse waves independently of the monitoring process in the surveillance device 25, and the pulse generation signal is obtained from the pulse generation sensor 35.

In another embodiment, the surveillance device 25 controls the electromechanical device 30 to generate the pulse waves (indicated by dashed line in Fig. 5), or

communicates with a controller for electromechanical device 30. In either case, the control signal for the electromechanical device 30 may be used as pulse generation signal, in addition to or instead of the output signal of the pulse generation sensor 35. In this embodiment, the pulse generation sensor 35 may thus be omitted.

Third monitoring concept

Fig. 6 illustrates a combination of system components according to the third monitoring concept. In the example of Fig. 6, the pulse generator 30 is an

electromechanical device which is attached to or included in the extracorporeal circuit 20 and operated to generate a pulse wave that propagates via the extracorporeal circuit into and through part of the vascular system to a pulse wave sensor 40 arranged in contact with or in proximity to the patient. All embodiments discussed above in relation to the first monitoring concept are equally applicable to the third monitoring concept, subject to the following modifications.

The electromechanical device 30 may be any inherent pulse generator in the extracorporeal circuit 20 or in the dialysis machine 200 as a whole, such one or more valves, one or more pumping devices, or a combination thereof. Generally, the pulse generator comprises at least the (or each) blood pump (e.g. 3 in Fig. 1) in the

extracorporeal circuit 20. Alternatively, the pulse generator may be a dedicated

electromechanical device which is included in the dialysis machine 200, e.g. attached to the blood line of the extracorporeal circuit 20, to generate the pulse waves.

The pulse generation sensor 35 may be any existing sensor in the extracorporeal circuit 20 or the dialysis machine 200 that is capable of generating a pulse generation signal. Such a sensing device may include at least one of the pressure sensors 4a-4c, or the pump sensor 26. Alternatively, a control signal for the pulse generator 30 may be used as pulse generation signal, in addition to or instead of the output signal of the pulse generation sensor 35. Such a control signal may be obtained from a separate control unit (e.g. 23 in Fig. 1), unless generated by the surveillance device 25 itself. In this alternative

embodiment, the pulse generation sensor 35 may thus be omitted.

In yet another alternative, the pulse generation sensor 35 may be a dedicated sensor that is arranged in contact with or in proximity to the extracorporeal circuit 20 to detect the pulse wave. Such a dedicated pulse wave sensor may be of any suitable kind, including a pressure sensor or a PPG sensor.

The pulse wave sensor 40 is arranged in contact with or in proximity with the patient's body to detect the pulse waves. The pulse wave sensor 40 may be of any suitable kind, including a pressure sensor or a PPG sensor.

Below as few implementation examples are given, which are applicable to either monitoring concept.

Fig. 7 illustrates an inflatable cuff 70 which includes a pulse generator 71 and a sensor 72. The inflatable cuff 70 is preferably connected to the surveillance device 25 which may be configured to, directly or indirectly, control the inflation of the cuff. For example, as indicated, the cuff 70 may be connected via tubing 73 to a pump (not shown) which is operable to supply gas to and release gas from the cuff via the tubing 73. The pump may or may not be part of the dialysis machine 200. In a variant, the pulse generator 71 is a mechanical generator, e.g. a vibrator, localized in the cuff 70. The cuff 70 may also be connected such that the surveillance device 25 may control the activation of the pulse generator 71 (by inflation of the cuff 70 or, alternatively, by activation of the localized mechanical generator), as well as sample data from the sensor 72. The sensor 72 may be at least one of an electrode for measurement of an electrical property, a pressure sensor and a PPG sensor. Alternatively, the sensor 72 may be located in the dialysis machine to remotely measure the gas pressure in the cuff 70 via the tubing 73. As will be understood from the following, the cuff 70 may contain more or less of the functionality indicated in Fig. 7 depending on monitoring concept.

The cuff 70 may be used in either monitoring concept as the above-mentioned calibration device for generating absolute values of the blood pressure in the patient during monitoring.

The cuff 70 may be used in the first and second monitoring concepts as the pulse generation sensor 35, by the surveillance device 25 obtaining measurement data from the sensor 72.

The cuff 70 may be used in the second monitoring concept as the pulse generator 30, wherein a pulse wave may be generated by intermittently controlling the pump, while the cuff is inflated, to supply a pulse of pressurized gas through the tubing 73. Alternatively, the pulse generator 71 may be selectively activated to generate a pulse wave. The pulse generator 71 may be any type of suitable electromechanical device.

The cuff 70 may be used in the third monitoring concept as the pulse wave sensor 40, by the surveillance device 25 obtaining data from the sensor 72. For use in the third monitoring concept, the sensor 72 is typically not an electrode, since such an electrode generally cannot detect a pulse wave that has propagated through the vascular system of the patient. However, the sensor might be an electrode of a bioimpedance measurement device, which is configured to detect volumetric changes in the patient's vascular system.

Fig. 8 is a block diagram to illustrate an embodiment of the surveillance device 25. The device 25 includes the data acquisition part 28 which is configured to sample data from the pulse generation sensor 35 (or a control unit, as described above) and the pulse wave sensor 40 and generate input or measurement signals to the data analysis part 29. The data analysis part 29 includes a block 801 which receives the input signal obtained from the pulse wave sensor 40 and processes the input signal for generation of a first monitoring signal. The first monitoring signal contains pulses that represents the pulse waves detected by the pulse wave sensor 40, and is suitably essentially free of interfering signals (such as pump pulses or physiological pulses, depending on monitoring concept). By "essentially free" is meant that the interfering signals are removed from the input signal to such an extent that the pulse wave from the pulse generator 30 may be detected and analysed for the purpose of monitoring. Block 801 may be configured to implement the signal processing described in Section IV below, or another signal processing. The data analysis part 29 may also include a block 802 which receives and processes the input signal obtained from the pulse generation sensor 35 to generate a second monitoring signal.

Depending on monitoring concept and implementation, the block 802 may be configured to implement the signal processing described in Section IV below, another signal processing, or be omitted. The data analysis part 29 also comprises a block 803 which receives the first and second monitoring signals from blocks 801 and 802 and which calculates the blood pressure value based on the time difference. Thus, block 803 may e.g. implement steps 301-306 in Fig. 3. The device 25 further includes a data output part 804, which receives and outputs the blood pressure value.

In Fig. 8, the data analysis part 29 also includes a pulse prediction block 810 which implements a step for obtaining a pulse profile which is a predicted temporal profile of pumping pulses generated in the extracorporeal circuit. The pulse prediction block 810 may operate on data from a database DB (a reference library). The resulting pulse profile may be provided to blocks 801, 802, which may be configured to use the pulse profile for time domain filtering, as will be explained in detail below.

The data analysis part 29, and thus blocks 801-803 and 810, may be implemented by software instructions that are executed by a processing device, such as a general- or special-purpose computer device or a programmed microprocessor. However, it is conceivable that some or all blocks are fully or partially implemented by dedicated hardware, such as an FPGA, an ASIC, or an assembly of discrete electronic components (resistors, capacitors, operational amplifier, transistors, etc), as is well-known in the art.

III. PHYSIOLOGICAL PHENOMENA AND SENSORS

In principle, embodiments of first monitoring concept may use pulse waves from any type of physiological phenomenon, be it occasional, repetitive or cyclical (i.e. periodic). However, in certain situations, it may be easier to isolate a series of pressure pulses from a repetitive or cyclical physiological phenomenon in the pulse wave signal, since one pressure pulse may be used to identify another pressure pulse in the series based on an approximate, estimated or predicted temporal relation between the two pulses.

Occasional physiological phenomena include reflexes, sneezing, voluntary muscle contractions, and non-voluntary muscle contractions.

Periodic physiological phenomena include heartbeats and breathing (respiration). Heartbeats normally occur with a frequency of in the range of about 0.5-3 Hz, whereas breathing has a frequency of about 0.15-0.4 Hz, with frequencies typically centred around -0.25 Hz. The present Assignee has found that the breathing of the patient causes a corresponding modulation of the pressure in the extracorporeal circuit, and that such a modulation may be detected by at least one pulse wave sensor in the circuit.

Normally, the arterial blood pressure is modulated by 4 mmHg to 6 mmHg in a wavelike manner during respiration. Deep respiration may result in blood pressure variation of 20 mmHg.

The breathing-induced modulation of the arterial blood pressure in the subject has several reasons:

"Cross-talk" between different parts of the sympathetic control system of the brain. Signals of the respiratory centre spill over to the centre controlling the vasomotor status causing blood pressure variations, the vasomotor referring to actions upon a blood vessel which alter its diameter by contraction and dilatation.

Breathing modulates the heart rate which modulates cardiac output and blood pressure.

- Modulation of cardiac output due to variations of the pressure in the thoracic cavity during breathing. At inspiration the left ventricle of the heart is supplied with a smaller blood volume since more blood is contained in the blood vessels in the chest at the expense of the pump volume of the heart. Blood pressure will then change as the cardiac output varies.

- Excitation of baroreceptors of the heart due to respiration. This will cause

modulation of blood pressure since the sympathetic system will respond to the stretch of the baroreceptors by changing the blood pressure.

The hydro- static pressure change due to the rise and fall of the chest during respiration of a subject in supine position. At inspiration the centre of gravity is elevated which causes increased pressure.

If the physiological phenomenon is the patient's heartbeat, the pulse generation sensor 35 may be any device that detects the electrical activity of the heart over time. One such pulse generation sensor is an ECG device, which is connected to the patient via skin electrodes that detect electric currents produced by the patient's heart. A similar device may be used to detect corresponding electrical activity in the patient's body resulting from any other physiological phenomenon, such as breathing or an occasional phenomenon as mentioned above. Such a device is often referred to as a "myoelectric sensor" or a

"myoelectrogram", and the resulting measurement signal is often referred to a myoelectric signal or a motor action potential.

It should be understood that the pulse generation sensor 35 may contain a plurality of sub-units. For example, a myoelectric sensor often contains two or more electrodes, some of which may be placed in contact with the patient's body, while others may be placed in contact with an electrical reference, such ground potential.

In an alternative embodiment of the first monitoring concept, the pulse generation sensor 35 is instead included in the extracorporeal circuit 20 or the dialysis fluid circuit 20' to detect the electrical activity of the heart over time. Such a pulse generation sensor may be designed to detect the patient's electrical voltages transmitted from the access devices 1, 14 to the pulse generation sensor 35 via the blood, via electrically conductive blood tubing or on other conductive pathways. The use of such a pulse generation sensor in an extracorporeal blood circuit for the purpose of detecting disconnection of an access device from the blood access of a patient is disclosed in US2007/0000847, which is incorporated herein by this reference. The use of such a pulse generation sensor thus enables monitoring of the patient's blood pressure based solely on sensors in the extracorporeal circuit 20. When using a pulse generation sensor 35 in the extracorporeal circuit 20 to detect the electrical activity of the heart, signal interferences/noise may be caused by myoelectric signals which are generated by muscular activity in the arm close to the blood vessel access. Thus, the measurement signal of the pulse generation sensor 35 may contain not only signal components originating from the heart, but also myoelectric signal

components. One way to improve the signal quality of the measurement signal is to configure the pulse generation sensor 35 to measure electrical voltages at two different locations in the extracorporeal circuit 20, e.g. on both the venous side and the arterial side of the circuit 20. This results in two measurement signals, in which the signal components that originate from the heart essentially coincide in time (since they propagate through the vascular system at the propagation speed of electromagnetic waves). On the other hand, the myoelectric signal components are essentially identical in the two measurement signals but are mutually phase shifted. This is further illustrated in Fig. 16, which shows myoelectric signals 161, 162 recorded at two locations narrowly spaced on a muscle. It may be seen that the signals 161, 162 are similar in shape but phase shifted. The phase shift is caused by the fact that the myoelectric signals propagate as electrochemical waves at low speed through the nervous system to the different measurement locations, which are at different distance to the origin of the myoelectric signals. The phase shift makes it possible (e.g. in block 802) to identify and subtract the myoelectric signal components, while leaving the heart signal essentially unaffected.

In an alternative embodiment, a similar pulse generation sensor 35 is arranged in the extracorporeal circuit 20 to detect electrical activity in the patient' s body resulting from any other physiological phenomenon, such as breathing or an occasional phenomenon as mentioned above.

Furthermore, if the physiological phenomenon is the patient's breathing or a related occasional phenomenon, a conventional respiration sensor may be used as pulse generation sensor. Such a respiration sensor may involve strain gauges that are strapped around the patient's chest or abdomen, so as to convert the expansion and contraction of the rib cage or abdominal area into an electrical signal. Another example of such a respiration sensor is a sensor sheet, known from JP10-14889A, in which internal electrostatic capacitances in the sensor sheet vary in correspondence with the patient's breathing, when the patient lies on the sensor sheet. The variations in electrostatic capacitances may be converted into an electrical signal which is representative of the patient's breathing, or an occasional phenomenon, as applicable. There are many other suitable types of respiration sensors, such as capnographs.

IV. SIGNAL PROCESSING OF PRESSURE SIGNAL

It should be understood that the pulse wave signal and/or the pulse generation signal may include significant interferences and artefacts, which may obscure the relevant pulse. Thus, the pulse wave signal and/or pulse generation signal may be subjected to a signal analysis process for extraction of the relevant pulse.

This Section describes various embodiments of such a signal analysis process in relation to measurement data from a pressure sensor, i.e. a pressure signal, specifically when the pressure sensor implements the pulse wave sensor 40 in the first monitoring concept (Fig. 2).

It should be realized that, depending on implementation, such a pressure signal may be obtained from the pulse wave sensor 40 and/or the pulse generation sensor 35 in any one of the first, second and third monitoring concepts. It is to be understood that these pressure signals may be processed in analogy with the following examples. This also applies to measurement data from other types of sensors, such as a PPG sensor.

Fig. 9(a) shows an example of a pressure signal in the time domain, and Fig. 9(b) shows the corresponding energy spectral density, i.e. signal amplitude as a function of frequency. The energy spectral density reveals that the detected pressure signal contains a number of different frequency components emanating from the blood pump (3 in Fig. 1). In the illustrated example, there is a frequency component at the base frequency (f₀) of the blood pump (at 1.5 Hz in this example), as well as its harmonics 2f₀, 3f₀ and 4f₀. The base frequency, also denoted pumping frequency in the following, is the frequency of the pump strokes that generate pulse waves in the extracorporeal blood flow circuit. For example, in a peristaltic pump of the type shown in Fig. 1, two pump strokes are generated for each full revolution of the rotor 3', i.e. one pump stroke for each roller 3a, 3b. Fig. 9(b) also indicates the presence of a frequency component at half the pumping frequency (0.5f₀) and harmonics thereof, in this example at least f₀, 1.5fo, 2f₀ and 2.5f₀. Fig. 9(b) also shows a heart signal (at 1.1 Hz) which in this example is approximately 40 times weaker than the blood pump signal at the base frequency fo. Although not shown in Fig. 9, the pressure signal may also contain pressure pulses originating from other mechanical pulse generators (not shown) in the circuit 20, such a valves, a pump for dialysis fluid, etc. In the following, disturbances caused by pulse generators in or associated with the extracorporeal circuit 20 are collectively denoted "pressure artefacts", "pump pulses" or "interference pulses".

Still further, as explained above, more than one physiological phenomenon in the patient may give rise to pressure pulses in the pressure signal. Such physiological phenomena include the breathing system, the autonomous system for blood pressure regulation and the autonomous system for body temperature regulation. In certain situations, it may be desirable to process the pressure signal for isolation of pressure pulses originating from a specific one of the physiological phenomena.

Fig. 10 is a flow chart that illustrates steps of a signal analysis process 1000 according to an embodiment of the present invention. It is initiated by acquiring a pressure signal, step 1001, e.g. from the venous or the arterial pressure sensor. The signal analysis process may be divided into a number of main steps: a pre-processing step 1002, a signal extraction step 1003 and an analysis step 1004. The pre-processing step 1002 includes elimination or reduction of signal noise, e.g. measurement noise, and signal offset, as detailed in the section above relating to the data acquisition part 28. The signal extraction step 1003 may conceptually be separated into two sub-steps: an elimination or reduction of pressure artefacts originating from pulse generators in (or associated with) the

extracorporeal circuit (step 1003') and an isolation of pressure data originating from a relevant physiological phenomenon (step 1003")· In the context of the present disclosure, the signal extraction step 1003 denotes a process of generating a time-dependent signal (also denoted "monitoring signal" herein) which is free or substantially free from any unwanted pressure modulations.

It should be noted that the steps 1002, 1003', 1003" may be executed in any order, and also that the functionality of one step may be included in another step. For instance, the pressure signal may be band-pass filtered or low-pass filtered to isolate a breathing signal, in a way such that signal noise and/or signal offset and/or pressure artefacts are eliminated from the pressure signal. Furthermore, any of steps 1002, 1003' and 1003" may be omitted, depending on the amount of signal interference and the required quality of the resulting monitoring signal.

In the analysis step 1004, the timing of one or more physiological pulses in the monitoring signal is determined, by applying a dedicated signal analysis algorithm for extraction of a characteristic time point for one or more pulses. In step 1005, the time point is output, for use in determining the patient's blood pressure, e.g. according to step 305 in Fig. 3.

In the following, different embodiments of the signal extraction part 1003 will be exemplified and described in further detail.

Elimination of artefacts (step 1003')

In the simplest case, no pump or other source of pressure artefacts is present in the extracorporeal circuit 20 connected to the patient during the data acquisition. For instance, the pump may have been shut down. In such a case, step 1003' may be omitted.

In the general case, however, one or more pumps are running or other sources of cyclic or non-cyclic, repetitive or non-repetitive artefacts are present during the data acquisition. Information on cyclic disturbances may be known from external sources, e.g. other sensors (e.g. the pump sensor 26 in Fig. 1), or may be estimated or reconstructed from system parameters.

Cyclic pressure artefacts may originate from operating one or more blood pumps, and further pumps such as pumps for dialysis fluid, repetitive actuation of valves, and movements of membranes in balancing chambers. According to the findings in connection with the present invention, artefacts may also originate from mechanical resonance of system components such as swinging movements of bloodlines energized by e.g. a pump. Frequencies of bloodline movements are given by the tube lengths and harmonics thereof and by the beating between any frequencies involved, i.e. between different self- oscillations and pump frequencies. These frequencies may differ between the venous and arterial lines. Mechanical fixation of the bloodlines and other free components may remedy the problem of mechanical resonance. Alternatively, an operator may be instructed to touch or jolt the blood lines to identify natural frequencies associated with the blood lines, which information may be used in the analysis for improved removal of components not belonging to the pressure data of interest.

Examples of non-cyclic artefacts are subject movement, valve actuation, movements of tubing, etc.

Elimination of artefacts may, e.g., be provided by:

Controlling a pulse generator in the extracorporeal circuit, such as a pump

o By temporarily shutting down the pulse generator;

o Shifting the pulse generator frequency;

- Low pass, band pass or high pass filtering;

Spectral analysis and filtering in the frequency domain;

Time domain filtering.

Controlling a pulse generator

Artefacts from a pulse generator, such as a pump, in the extracorporeal circuit may be avoided by temporarily shutting down (disabling) the pulse generator, or by shifting the frequency of the pulse generator away from frequencies of the relevant physiological phenomenon.

A feedback control with respect to the physiological phenomenon, e.g. using the pulse generation signal of the pulse generation sensor 35 (Fig. 2), may be used to set the pump frequency optimally for detection of pressure pulses originating from the

physiological phenomenon. Hence, control unit 23 of Fig. 1 may be operated to control the pump frequency based on the pulse generation signal in order to facilitate the detection of the pressure pulses, i.e. the pump frequency is controlled to minimize any overlap in frequency between the pulses originating from the pump and the pulses originating from the relevant physiological phenomenon. For example, the pump frequency may be periodically increased and decreased around the overlap frequency, so as to maintain the overall blood flow rate. Applying low pass, band pass or high pass filters

The input signal to step 1003' may be fed into a filter, e.g. digital or analog, with frequency characteristics, such as frequency range and/or centre of frequency range, matched to the frequencies generated by a pulse generator, such as the blood pump 3 (Fig. 1), in the extracorporeal circuit. For instance, in a case where the blood pump operates within the frequency range of 1 Hz, a suitable low pass filter may be applied in order to remove pressure artefacts above 1 Hz while retaining frequency components of a physiological phenomenon below 1 Hz. Correspondingly, a high pass filter may be applied to retain frequency components of a physiological phenomenon above a frequency of the pulse generator. Alternatively, one or more notch filters or the like may be utilised to remove/attenuate frequencies in one or more confined ranges.

Spectral analysis and filtering in the frequency domain

The input signal to step 1003' may be subjected to spectral analysis, e.g. by applying a Fourier transformation technqiue, such as FFT (Fast Fourier Transform) to convert the input signal into the frequency domain. The resulting energy spectrum (amplitude spectrum) may then be multiplied by an appropriate filter function and then re-transformed into the time domain. There are many alternative and equivalent filtering techniques available to the skilled person.

Time domain filtering

Artefact elimination by filtering in the time domain is further disclosed and exemplified further below under Sections V and VI. In addition to Sections V and VI, reference is also made to WO2009/156175, which is incorporated herein in its entirety by this reference.

By filtering the pressure signal in the time domain, it is possible to essentially eliminate artefacts, even if the artefacts and physiological pulses overlap or nearly overlap in the frequency domain, and even if the physiological pulses are much smaller in amplitude than the artefacts. A frequency overlap is not unlikely, e.g. if one or both of the artefacts and the physiological pulses is made up of a combination of frequencies or frequency ranges. By "essentially eliminating" is meant that the artefacts are removed from the pressure signal to such an extent that the physiological pulses may be detected and analysed for the purpose of monitoring the blood pressure.

Furthermore, the frequency, amplitude and phase content of the artefacts and the physiological pressure pulses may vary over time. For example, such variations are known occur in the heart rhythm. In healthy subjects under calm conditions, variations in heart rhythm (heart rate variability, HRV) may be as large as 15%. Unhealthy subjects may suffer from severe heart conditions such as atrial fibrillation and supraventricular ectopic beating, which may lead to an HRV in excess of 20%, and ventricular ectopic beating, for which HRV may be in excess of 60%. These heart conditions are not uncommon among, e.g., dialysis patients.

Any frequency overlap may make it impossible or at least difficult to remove artefacts by conventional filtering in the frequency domain. Furthermore, frequency variations may make it even harder to successfully remove artefacts, since the frequency overlap may vary over time. Even in the absence of any frequency overlap, frequency variations may make it difficult to define filters in the frequency domain.

Still further, the time domain filtering may make it possible to remove artefacts for individual physiological pulses, and may thus improve the response time compared to filtering in the frequency domain, which may need to operate on a sequence of artefacts and physiological pulses in the pressure signal.

Isolation of pressure data from a physiological phenomenon (step 1003")

Isolating pressure data originating from a relevant physiological phenomenon may be provided by any or a combination of:

Low pass, band pass or high pass filtering;

Spectral analysis and filtering in the frequency domain; or

Time domain filtering. Applying low pass, band pass or high pass filters

The input signal to step 1003" may be fed into a filter, e.g. digital or analog, with frequency characteristics, such as frequency range and/or centre of frequency range, matched to the frequencies of a signal of relevant physiological phenomenon where e.g. in case the isolation concerns:

- Breathing, a frequency range of about 0.15 - 0.4 Hz may be allowed to pass the filter;

Heart, a frequency range of about 0.5 -3 Hz may be allowed to pass the filter. The filter may include one or more of a low pass filter, a band pass filter, a high pass filter, a bandstop filter, a notch filter and other similar or equivalent filters.

According to an alternative, the surveillance device 25 is configured to set the cut-off frequency or frequencies of the filter, at least in part, based on patient- specific information, i.e. existing data records for the patient, e.g. obtained in earlier treatments of the same patient. The patient-specific information may be stored in an internal memory of the surveillance device 25, on an external memory which is made accessible to the surveillance device, or on a patient card where the information is e.g. transmitted wirelessly to the surveillance device, e.g. by RFID (Radio Frequency IDentification).

Spectral analysis and filtering in the frequency domain

The input signal may be subjected to spectral analysis, e.g. by applying a Fourier transformation technique, such as FFT (Fast Fourier Transform) to convert the input signal into the frequency domain. The resulting energy spectrum (amplitude spectrum) may then be multiplied by an appropriate filter function and then re-transformed into the time domain. There are many alternative and equivalent filtering techniques available to the skilled person. Time domain filtering

Pressure data originating from a specific physiological phenomenon may be extracted as an error signal of an adaptive filter. The adaptive filter is fed with both the input signal and a predicted signal profile of a cyclic disturbance. The cyclic disturbance may be pressure pulses from any of the other physiological phenomena (e.g. heart or breathing). Particularly, a reconstructed pressure profile originating from the heart or the breathing system of the patient may be input to the adaptive filter. This and other time domain filtering techniques for removing unwanted signal components from a

measurement signal is further disclosed and exemplified in Section VI below. Although Section VI is concerned with eliminating pressure artefacts originating from a pulse generator in an extracorporeal circuit, such as a pumping device, it is equally applicable for eliminating heart or breathing pulses originating from unwanted physiological phenomena, as long as it is possible to obtain a predicted signal profile of the heart or breathing pulses (also denoted "predicted physiological profile" in Section VI). The skilled person realizes that such a predicted signal profile may be obtained in ways equivalent to those described in Section V below. Such ways include using a signal profile which is fixed and predetermined, e.g. by simulation or reference measurement, using a signal profile which is intermittently updated based on reference measurements, using a signal profile which is obtained from a reference library based on one or more current system parameter values, and using a signal profile which is obtained by modifying a predetermined profile based on one or more current system parameter values. The system parameter values may relate to a rate of heart/breathing pulses, which may be derived from the pulse generation signal of the pulse generation sensor 35, and/or any of the system parameters listed in Section V.

V. OBTAINING A PUMP PROFILE

This Section describes different embodiments for predicting or estimating the signal profile of pump pulses in any one of the system configurations discussed herein. The predicted signal profile is typically given as a series of pressure values over a period of time normally corresponding to at least one complete pump cycle (pump stroke) of the blood pump 3.

Fig. 11 illustrates an example of a predicted signal profile u(n) for the system in Fig. 1. Since the blood pump 3 is a peristaltic pump, in which two rollers 3a, 3b engage a tube segment during a full revolution of the rotor 3 the pressure profile consists of two pump strokes. The pump strokes may result in different pressure values (pressure profiles), e.g. due to slight differences in the engagement between the rollers 3a, 3b and the tube segment, and thus it may be desirable for the predicted signal profile to represent both pump strokes. If a lower accuracy of the predicted signal profile may be tolerated, e.g. if the output of the subsequent removal process (see Section V) is acceptable, the predicted signal profile might represent one pump stroke only.

On a general level, the predicted signal profile may be obtained in a reference measurement, through mathematical simulation of the fluid system, or combinations thereof.

Reference measurement

A first main group of methods for obtaining the predicted signal profile is based on deriving a time-dependent reference pressure signal ("reference signal") from a pressure sensor in the system, typically (but not necessarily) from the same pressure sensor that provides the measurement signal (pressure signal) that is to be processed for removal of pump pulses. During this reference measurement, the pump pulses are prevented from reaching the relevant pressure sensor, either by shutting down/deactivating the pulse generator 30 (e.g. in the second monitoring concept) or by isolating the pressure sensor from the pulse waves generated by the pulse generator 30. For example, the reference measurement may be carried out during a priming phase, in which the extracorporeal circuit 20 is detached from the patient and a priming fluid is pumped through the blood lines. Alternatively, the reference measurement may be carried in a simulated treatment with blood or any other fluid.

Optionally, the reference measurement may involve averaging a plurality of pump pulses to reduce noise. For example, a plurality of relevant signal segments may be identified in the reference signal, whereupon these segments are aligned to achieve a proper overlap of the pump pulses in the different segments and then added together. The identifying of relevant signal segments may be at least partially based on timing information which indicates the expected position of each pump pulse in the reference signal. The timing information may be obtained from a trigger point in the output signal of the pump sensor 26, in a control signal of the control unit 23, or in the pressure signal from another one of the pressure sensors 4a- 4c. For example, a predicted time point of a pump pulse in the reference signal may be calculated based on a known time delay between the trigger point and the pressure sensor that generates the reference signal. In variant, if the pump pulses are periodic, relevant signal segments may be identified by identifying crossing points between the reference signal and a given signal level, wherein the relevant signal segments are identified to extend between any respective pairs of crossing points.

In a first embodiment, the predicted signal profile is directly obtained in a reference measurement before the extracorporeal circuit 20 is connected to the patient, and is then used as input to the subsequent removal process, which is executed when the extracorporeal circuit 20 is connected to the patient. In this embodiment, it is thus assumed that the predicted signal profile is representative of the pump pulses when the system is connected to the patient. Suitably, the same pump frequency/speed is used during the reference measurement and during the removal process. It is also desirable that other relevant system parameters are maintained essentially constant. Fig. 12 is a flow chart of a second embodiment. In the second embodiment, a reference library or database is first created based on the reference measurement (step 1201). The resulting reference library is typically stored in a memory unit, e.g. RAM, ROM, EPROM, HDD, Flash, etc (cf. DB in Fig. 8) in the surveillance device 25. During the reference measurement, reference pressure signals are acquired for a number of different operational states of the extracorporeal circuit. Each operational state is represented by a unique combination of system parameter values. For each operational state, a reference profile is generated to represent the signal profile of the pump pulses. The reference profiles together with associated system parameter values are then stored in the reference library, which is implemented as a searchable data structure, such as a list, look-up table, search tree, etc.

During the actual monitoring process, i.e. when pump pulses are to be eliminated from the pressure signal, current state information indicating the current operational state of the extracorporeal circuit 20 is obtained from the system, e.g. from the pump sensor 26, the control unit 23 or otherwise (step 1202). The current state information may include a current value of one or more system parameters. The current value is then matched against the system parameter values in the reference library. Based on the matching, one or more reference profiles are selected (step 1203) and used for preparing the predicted signal profile (step 1204).

Generally, the aforesaid system parameters represent the overall system state, including but not limited to the structure, settings, status and variables of the dialysis machine 200 or its components. In the system of Fig. 1, exemplary system parameters may include:

Pump-related parameters: number of active pumps connected directly or indirectly (e.g. in a fluid preparation system for the dialyser) to the extracorporeal circuit, type of pumps used (roller pump, membrane pump, etc), flow rate, revolution speed of pumps, shaft position of pump actuator (e.g. angular or linear position), etc

Dialysis machine settings: temperature, ultrafiltration rate, mode changes, valve position/changes, etc

Disposable dialysis equipment/material: information on pump chamber/pump segment (material, geometry and wear status), type of blood line (material and geometry), type of dialyser, type and geometry of access devices, etc

Dialysis system variables: actual absolute pressures of the system upstream and downstream of the blood pump, e.g. venous pressure (from sensor 4c), arterial pressure (from sensor 4a) and system pressure (from sensor 4b), gas volumes trapped in the flow path, blood line suspension, fluid type (e.g. blood or dialysis fluid), etc

Patient status: blood access properties, blood properties such as e.g. hematocrit, plasma protein concentration, etc It is to be understood that any number or combination of system parameters may be stored in the reference library and/or used as search variables in the reference library during the monitoring process.

In the following, the second embodiment will be further explained in relation to a number of examples. In all of these examples, the pump revolution frequency ("pump frequency"), or a related parameter (e.g. blood flow rate) is used to indicate the current operational state of the extracorporeal circuit 20 during the monitoring process. In other words, the pump frequency is used as search variable in the reference library. The pump frequency may e.g. be given by a set value for the blood flow rate output from the control unit 23, or by an output signal of the pump sensor 26. Alternatively, the pump frequency may be obtained by frequency analysis of the pressure signal from any of the sensors 4a-4c (Fig. 1) during operation of the fluid system. Such frequency analysis may be achieved by applying any form of harmonics analysis to the pressure signal, such as Fourier or wavelet analysis. As indicated in Fig. 9(b), the base frequency f₀ of the pump may be identified in a resulting power spectrum.

In the following, three examples are given of techniques for generating the predicted signal profile by accessing such a reference library.

In a first example, the reference profiles stored in the reference library are temporal profiles. The reference library is searched for retrieval of the reference profile that is associated with the pump frequency that lies closest to the current pump frequency. If no exact match is found to the current pump frequency, an extrapolation process is executed to generate the predicted signal profile. In the extrapolation process, the retrieved reference profile is scaled in time to the current pump cycle, based on the known difference ("pump frequency difference") between the current pump frequency and the pump frequency associated with the retrieved reference profile. The amplitude scale may also be adjusted to compensate for amplitude changes due to pump frequency, e.g. based on a known function of amplitude as a function of pump frequency. Fig. 11 illustrates a reference profile n(n) obtained at a flow rate of 470 ml/min, and a predicted signal profile u(n) which is obtained by scaling the reference profile to a flow rate of 480 ml/min. For comparison only, a reference profile r_actuai(n) obtained at 480 ml/min is also shown, to illustrate that extrapolation process indeed may yield a properly predicted signal profile.

In a second example, the reference profiles stored in the reference library are temporal profiles. The reference library is again searched based on current pump frequency. If no exact match is found to the current pump frequency, a combination process is executed to generate the predicted signal profile. Here, the reference profiles associated with the two closest matching pump frequencies are retrieved and combined. The combination may be done by re- scaling the pump cycle time of the retrieved reference profiles to the current pump frequency and by calculating the predicted signal profile via interpolation of the re- scaled reference profiles. For example, the predicted signal profile u(n) at the current pump frequency v may be given by: u(n) = g(v - v ^■ n(n) + (1- g(v - v_t))^■ η(η), wherein η(η) and η(η) denotes the two retrieved reference profiles, obtained at a pump frequency v, and Vj, respectively, after re-scaling to the current pump frequency v, and g is a relaxation parameter which is given as a function of the frequency difference (v - Vj), wherein Vi≤ v < Vj and 0 < g < 1. The skilled person realizes that the predicted signal profile u(n) may be generated by combining more than two reference profiles.

Fig. 13(a) illustrates a predicted signal profile u(n) at a current flow rate of 320 ml/min for a pressure signal obtained from the venous sensor 4c in the system of Fig. 1. The predicted signal profile u(n) has been calculated as an average of a reference profile ri{n) obtained at a flow rate of 300 ml/min from the venous sensor and a reference profile r₂(n) obtained at a flow rate of 340 ml/min from the venous sensor. For comparison only, a reference profile r_actuai(n) obtained at 320 ml/min is also shown, to illustrate that the combination process indeed may yield a properly predicted signal profile. In fact, the differences are so small that they are only barely visible in the enlarged view of Fig. 13(b).

The first and second examples may be combined, e.g. by executing the extrapolation process of the first example if the pump frequency difference is less than a certain limit, and otherwise executing the combination process of the second example.

In a third embodiment, like in the second embodiment shown in Fig. 12, a number of reference signals are acquired in the reference measurement, wherein each reference signal is obtained for a specific combination of system parameter values. The reference signals are then processed for generation of reference spectra, which are indicative of the energy and phase angle as function of frequency. These reference spectra may e.g. be obtained by Fourier analysis, or equivalent, of the reference signals. Corresponding energy and phase data are then stored in a reference library together with the associated system parameter values (cf. step 1201 in Fig. 12). The implementation of the reference library may be the same as in the second embodiment.

During the actual monitoring process, i.e. when pump pulses are to be eliminated from the pressure signal, a current value of one or more system parameters is obtained from the extracorporeal circuit (cf. step 1202 in Fig. 12). The current value is then matched against the system parameter values in the reference library. Based on the matching, a specific set of energy and phase data may be retrieved from the reference library to be used for generating the predicted signal profile (cf. step 1203 in Fig. 12). The predicted signal profile may be temporal and may be generated by adding sinusoids of appropriate frequency, amplitude and phase, according to the retrieved energy and phase data (cf. step 1204 in Fig. 12). Generally speaking, without limiting the present disclosure, it may be advantageous to generate the predicted signal profile from energy and phase data when the pump pulses (to be removed) contain only one or a few base frequencies (and harmonics thereof), since the predicted signal profile may be represented by a small data set (containing energy and phase data for the base frequencies and the harmonics). One the other hand, when the power spectrum of the pump pulses is more complex, e.g. a mixture of many base frequencies, it may instead be preferable to generate the predicted signal profile from one or more temporal reference profiles.

Fig. 14(a) represents an energy spectrum of a reference signal acquired at a flow rate of 300 ml/min in the system of Fig. 1. In this example, the reference signal essentially consists of a basic pump frequency at 1.2 Hz (fo, first harmonic) and a set of overtones of this frequency (second and further harmonics). Compared to the power spectrum of Fig. 9(b), the pressure signals used for generating the graphs in Fig. 14(a)- 14(d) do not contain any significant frequency component at 0.5f₀ and its harmonics. The graph in Fig. 14(a) displays the relative energy distribution, wherein the energy values have been normalized to the total energy for frequencies in the range of 0-10 Hz. Fig. 14(b) represents energy spectra of reference signals acquired at three different flow rates in the system of Fig. 1. The energy spectra are given in logarithmic scale versus harmonic number (first, second, etc). As shown, an approximate linear relationship may be identified between the logarithmic energy and harmonic number for the first four to five harmonic numbers. This indicates that each energy spectrum may be represented by a respective exponential/polynomial function. Fig. 14(c) illustrates the data of Fig. 14(b) in linear scale, wherein a respective polynomial function has been fitted to the data. As indicated in Figs 14(a)- 14(c), the energy spectra may be represented in different formats in the reference library, e.g. as a set of energy values associated with discrete frequency values or harmonic numbers, or as an energy function representing energy versus

frequency/harmonic number.

Fig. 14(d) illustrates a phase angle spectrum acquired together with the energy spectrum in Fig. 14(a), i.e. for a flow rate of 300 ml/min. The graph in Fig.14(d) illustrates phase angle as a function of frequency, and a linear function has been fitted to the data. In an alternative representation (not shown), the phase spectrum may be given as a function of harmonic number. Like the energy spectra, the phase spectra may be represented in different formats in the reference library, e.g. as a set of phase angle values associated with discrete frequency values or harmonic numbers, or as a phase function representing phase angle versus frequency/harmonic number.

From the above, it should be understood that the energy and phase data that are stored the reference library may be used to generate the predicted signal profile. Each energy value in the energy data corresponds to an amplitude of a sinusoid with a given frequency (the frequency associated with the energy value), wherein the phase value for the given frequency indicates the proper phase angle of the sinousoid. This method of preparing the predicted signal profile by combining (typically adding) sinusoids of appropriate frequency, amplitude and phase angle allows the predicted signal profile to include all harmonics of the pump frequency within a desired frequency range.

When a predicted signal profile is to be generated, the reference library is first searched based on a current value of one or more system parameters, such as the current pump frequency. If no exact match is found in the reference library, a combination process may be executed to generate the predicted signal profile. For example, the two closest matching pump frequencies may be identified in the reference library and the associated energy and phase data may be retrieved and combined to form the predicted signal profile. The combination may be done by interpolating the energy data and the phase data. In the example of Figs

14(a)- 14(d), an interpolated energy value may be calculated for each harmonic number, and similarly an interpolated phase value may be calculated for each harmonic number. Any type of interpolation function may be used, be it linear or non-linear.

In the first, second and third embodiments, one and the same pressure sensor is suitably used in both the reference measurement and the actual monitoring process. Alternatively, different pressure sensor units may be used, provided that the pressure sensor units yield identical signal responses with respect to the pump pulses or that the signal responses may be matched using a known mathematical relationship.

To further improve the first, second and third embodiments, the process of generating the predicted signal profile may also involve compensating for other potentially relevant factors that differ between the reference measurement and the current operational state. These so-called confounding factors may comprise one or more of the system parameters listed above, such as absolute average venous and arterial pressures, temperature, blood

hematocrit/viscosity, gas volumes, etc. This compensation may be done with the use of predefined compensation formulas or look-up tables.

In further variations, the second and third embodiments may be combined, e.g. in that the reference library stores not only energy and phase data, but also reference profiles, in association with system parameter value(s). When an exact match is found in the library, the reference profile is retrieved from the library and used as the predicted signal profile, otherwise the predicted signal profile is obtained by retrieving and combining (e.g.

interpolating) the energy and phase data, as in the third embodiment. In a variant, the predicted signal profile u(n) at the current pump frequency v is obtained by: u(n) = n(n) - /i(n) + /(n), wherein η(η) denotes a reference profile that is associated with the closest matching pump frequency v, in the reference library, tfn) denotes a reference profile that is reconstructed from the energy and phase data associated with the closest matching pump frequency v, in the reference library, and /(n) denotes an estimated reference profile at the current pump frequency v. The estimated reference profile /(n) may be obtained by applying predetermined functions to estimate the energy and phase data, respectively, at the current pump frequency v based on the energy and phase data associated with the closest matching pump frequency ¼·. With reference to Figs 14(b)- 14(c), such a predetermined function may thus represent the change in energy data between different flow rates. Alternatively, the estimated reference profile /(n) may be obtained by retrieving and combining (e.g.

interpolating) energy and phase data for the two closest matching pump frequencies v, and Vj as in the third embodiment.

In a further variant, the reference measurement is made during regular operation of the extracorporeal circuit 20, instead of or in addition to any reference measurements made before regular operation (e.g. during priming or simulated treatments with blood). Such a variant presumes that it is possible to intermittently shut off the pulse generator 30. This approach may e.g. be applied in the second monitoring concept, where the pulse generator is an electromechanical device. Alternatively, if the reference signal is obtained from a different pressure sensor (than the one that provides the pressure signal), and if this pressure sensor is substantially isolated from the pulse waves generated by the pulse generator 30, the reference signal may be used for generating the predicted signal profile (optionally after

adjustment/modification for differences in confounding factors), which is then used for removing pump pulses from the pressure signal. For example, the pressure signal from the system sensor 4b in the circuit 20 of Fig. 1 may be essentially isolated from the physiological pulses that originate from the patient, and this pressure signal may thus be used as the reference signal.

Simulations

As an alternative to the use of reference measurements, the predicted signal profile may be obtained directly through simulations, i.e. calculations using a mathematical model of the extracorporeal circuit 20, based on current state information indicating the current operational state of the system. Such current state information may include a current value of one or more of the above-mentioned system parameters. The model may be based on known physical relationships of the system components (or via an equivalent representation, e.g. by representing the system as an electrical circuit with fluid flow and pressure being given by electrical current and voltage, respectively). The model may be expressed, implicitly or explicitly, in analytical terms. Alternatively, a numerical model may be used. The model may be anything from a complete physical description of the system to a simple function. In one example, such a simple function may convert data on the instantaneous angular velocity of the pump rotor Ύ to a predicted signal profile, using empirical or theoretical data. Such data on the

instantaneous angular velocity might be obtained from the pump sensor 26 in Fig. 1.

In another embodiment, simulations are used to generate reference profiles for different operational states of the system. These reference profiles may then be stored in a reference library, which may be accessed and used in the same way as described above for the second and third embodiments. It is also to be understood that reference profiles (and/or

corresponding energy and phase angle data) obtained by simulations may be stored together with reference profiles (and/or corresponding energy and phase angle data) obtained by reference measurement.

VI. TIME DOMAIN FILTERING

There are several different ways of removing one or more pump pulses from the pressure/input signal, using a predicted signal profile of the pump pulses (e.g. obtained as described in Section V above). Here, two different removal processes will be described: Single Subtraction and Adaptive Filtering. Of course, the description of removal processes and their implementations is not comprehensive (neither of the different alternatives, nor of the implementations), which is obvious to a person skilled in the art.

Depending on implementation, the predicted signal profile may be input to the removal process as is, or the predicted signal profile may be duplicated to construct an input signal of suitable length for the removal process.

Single Subtraction

In this removal process, a single predicted signal profile is subtracted from the pressure signal. The predicted signal profile may be shifted and scaled in time and scaled in amplitude in any way, e.g. to minimize the error of the removal. Different minimization criterions may be used for such an auto-scaling, e.g., minimizing the sum of the squared errors, or the sum of the absolute errors. Alternatively or additionally, the predicted signal profile is shifted in time based on timing information that indicates the expected timing of the pump pulse(s) in the pressure signal. The timing information may be obtained in the same way as described above (cf. Section V) in relation to the averaging of pressure segments in the reference signal.

One potential limitation of this removal process is that the relationship between different frequencies in the predicted signal profile is always the same, since the process only shifts and scales the predicted signal profile. Thus, it is not possible to change the relationship between different harmonic frequencies, neither is it possible to use only some of the frequency content in the predicted signal profile and to suppress other frequencies. To overcome this limitation, adaptive filtering may be used since it uses a linear filter before subtraction, e.g. as described in the following.

Adaptive Filtering

Fig. 15 is a schematic overview of an adaptive filter 150 and an adaptive filter structure which is designed to receive the predicted signal profile u(n) and a pressure signal d(n), and to output an error signal e(n) which forms the aforesaid monitoring signal in which the pump pulses are removed.

Adaptive filters are well-known electronic filters (digital or analog) that self-adjust their transfer function according to an optimizing algorithm. Specifically, the adaptive filter 150 includes a variable filter 152, typically a finite impulse response (FIR) filter of length M with filter coefficients w(n).

Even if adaptive filters are known in the art, they are not readily applicable to cancel the pump pulses in the pressure signal d(n). In the illustrated embodiment, this has been achieved by inputting the predicted signal profile u(n) to the variable filter 152, which processes the predicted signal profile u(n) to generate an estimation signal d(n) , and to an adaptive update algorithm 154, which calculates the filter coefficients of the variable filter 152 based on the predicted signal profile u(n) and the error signal e(n). The error signal e(n) is given by the difference between the pressure signal d(n) and the estimation signal d(n) .

Basically, the calculation of the error signal e(n) involves a subtraction of the predicted signal profile u(n) from the pressure signal d(n), since each of the filter coefficients operates to shift and possibly re-scale the amplitude of the predicted signal profile u(n). The estimation signal d(n) , which is subtracted from the pressure signal d(n) to generate the error signal e(n), is thus formed as a linear combination of M shifted and amplitude- scaled predicted signal profiles u(n).

The adaptive update algorithm 154 may be implemented in many different ways, some of which will be described below. The disclosure is in no way limited to these examples, and the skilled person should have no difficulty of finding further alternatives based on the following description.

There are two main approaches to adaptive filtering: stochastic and deterministic.

The difference lies in the minimization of the error signal e(n) by the update algorithm 154, where different minimization criteria are obtained whether e(n) is assumed to be stochastic or deterministic. A stochastic approach typically uses a cost function / with an expectation in the minimization criterion, while a deterministic approach typically uses a mean. The squared error signal e (n) is typically used in a cost function when minimizing e(n), since this results in one global minimum. In some situations, the absolute error \e(n)\ may be used in the minimization, as well as different forms of constrained minimizations. Of course, any form of the error signal may be used, however convergence towards a global minimum is not always guaranteed and the minimization may not always be solvable.

In a stochastic description of the signal, the cost function may typically be according to,

J(n) = E {\ e(n) \² }, and in a deterministic description of the signal the cost function may typically be according to, J(n) =∑e² (n) .

The pump pulses will be removed in the estimation signal d(n) when the error signal e(n) (cost function J(n)) is minimized. Thus, the error signal e(n) will be cleaned from pump pulses while retaining the physiological pulses, once the adaptive filter 150 has converged and reached the minimum error.

In order to obtain the optimal filter coefficients w(n) for the variable filter 152, the cost function J needs to be minimized with respect to the filter coefficients w(n). This may be achieved with the cost function gradient vector Vj , which is the derivative of J with respect to the different filter coefficients wo, wi, WM-I - Steepest Descent is a recursive method (not an adaptive filter) for obtaining the optimal filter coefficients that minimize the cost function J. The recursive method is started by giving the filter coefficients an initial value, which is often set to zero, i.e., w(0) = 0. The filter coefficients is then updated according to, w(n + l) = w(n) +— μ [- ν J (n)] , where w is given by,

= Wy ... W_M_y X l .

Furthermore, the gradient vector Vj points in the direction in which the cost is growing the fastest. Thus, the filter coefficients are corrected in the direction opposite to the gradient, where the length of the correction is influenced through the step size parameter μ. There is always a risk for the Steepest Descent algorithm to diverge, since the algorithm contains a feedback. This sets boundaries on the step size parameter μ in order to ensure convergence. It may be shown that the stability criterion for the Steepest Descent algorithm is given by,

2

0 < u <

λ max where A™_ax is the largest eigenvalue of R, the correlation matrix of the predicted reference profile u(n), given by r(0) r(l) r( - 1)

r(l) r(0) r(M - 2)

R = E u (n) u (n) r( - 1) r( - 2) r(0) where u (n) is given by, u (n) = [u(n) u(n - l) ... u(n - M + l)] ^T x l .

If the mean squared error (MSE) cost function (defined by J = E || e(n) }) is used, it may be shown that the filter coefficients are updated according to,

(n + 1) = ¾>(η) + μ Ε[ u (n) e(n) ] , where e(n) is given e(n) = d(n) - u ^T(n) w(n)

The Steepest Descent algorithm is a recursive algorithm for calculation of the optimal filter coefficients when the statistics of the signals are known. However, this information is often unknown. The Least Mean Squares (LMS) algorithm is a method that is based on the same principles as the Steepest Descent algorithm, but where the statistics is estimated continuously. Thus, the LMS algorithm is an adaptive filter, since the algorithm is able to adapt to changes in the signal statistics (due to continuous statistic estimations), although the gradient may become noisy. Because of the noise in the gradient, the LMS algorithm is unlikely to reach the minimum error J_min, which the Steepest Descent algorithm does. Instantaneous estimates of the expectation are used in the LMS algorithm, i.e., the expectation is removed. Thus, for the LMS algorithm, the update equation of the filter coefficients becomes w(n + 1) = w(n) + μ u (n) e(n) .

The convergence criterion of the LMS algorithm is the same as for the Steepest Descent algorithm. In the LMS algorithm, the step size is proportional to the predicted signal profile u(n), i.e., the gradient noise is amplified when the predicted reference profile is strong. One solution to this problem is to normalize the update of the filter coefficients with

The new update equation of the filter coefficients is called the Normalized LMS, and given by w(n + l) = w(n) + ^■ u{n)e{n) ,

where 0<μ<2, and a is a positive protection constant.

There are many more different alternatives to the LMS algorithm, where the step modified. One of them is to use a variable adaptation step, w(n + 1) = w(n) + (n) u (n) e(n) , where a(n) for example may be, 1

(n)

n + c where c is a positive constant. It is also possible to choose independent adaptation steps for each filter coefficient in the LMS algorithm, e.g., according to, w(n + 1) = w(n) + Au(n) e(n) , where A is given by, l 0 0 · ·· 0

0 ₂ 0 · ·· 0

A = 0 0 ₃ ^■ ·· 0

0 0 0 ·

If instead the following cost function

is used, then the update equation becomes w(n + 1) = w(ri) + a sign[e(n)] u («) .

This adaptive filter is called the Sign LMS, which is used in applications with extremely high requirements on low computational complexity.

Another adaptive filter is the Leaky LMS, which uses a constrained minimization with the following cost function

This constraint has the same effect as if white noise with variance a was added to the predicted signal profile u(n). As a result, the uncertainty in the predicted signal profile u(n) is increased, which tends to hold the filter coefficients back. The Leaky LMS is preferably used when R, the correlation matrix of u(n), has one or more eigenvalues equal to zero. However, in systems without noise, the Leaky LMS makes performance poorer. The update equation of the filter coefficients for the Leaky LMS is given by, w(n + 1) = (1 - μα) w(n) + μ ιι (η) e(n) .

Instead of minimizing the MSE cost function as above, the Recursive Least Squares (RLS) adaptive filter algorithm minimizes the following cost function

J(n) =∑X-ⁱ \ e(i)

;=i where λ is called forgetting factor, 0 < λ < 1, and the method is called Exponentially Weighted Least Squares. It may be shown that the update equations of the filter coefficients for the RLS algorithm are, after the following initialization w(0) = 0_Mxl

P(0) = δ^~ι I where IM_XM is the identity matrix MxM, given according to

A^~lP(n - l) u (n)

k(n) =

\ + λ^~ι u ^T(n) P(n - l) u (n) ξ(η) = d(n) - w^T (n - \) u (n)

P(n) = λ^~ι P(n - 1) - λ^~ι k(n) ^T (n) P(n - 1) , where δ is a small positive constant for high signal-to-noise ratio (SNR), and a large positive constant for low SNR, 5«0.01a_u ², and ξ(η) corresponds to e(n) in the preceding algorithms. During the initialization phase the following cost function

is minimized instead, due to the use of the initialization P(0) = δ ¹1. The RLS algorithm converges in approximately 2M iterations, which is considerably faster than for the LMS algorithm. Another advantage is that the convergence of the RLS algorithm is independent of the eigenvalues of R, which is not the case for the LMS algorithm.

Several RLS algorithms running in parallel may be used with different λ and δ, which may be combined in order to improve performance, i.e., λ = 1 may also be used in the algorithm (steady state solution) with many different 5:s.

It should be noted that both the LMS algorithm and the RLS algorithm may be implemented in fixed-point arithmetic, such that they may be run on a processor that has no floating point unit, such as a low-cost embedded microprocessor or microcontroller.

Irrespective of implementation, the performance of the adaptive filter 150 may be further improved by switching the adaptive filter 150 to a static mode, in which the update algorithm 154 is disabled and thus the filter coefficients of the filter 152 are locked to a current set of values. The switching of the adaptive filter 150 may be controlled by an external process that analyses the physiological pulses in the error signal e(n), typically in relation to pump pulse data. The pump pulse data may be obtained from the pressure signal, a reference signal (see above), a dedicated pump sensor, a control unit for the blood pump, etc. The adaptive filter 150 may be switched into the static mode if the external process reveals that the rate of physiological pulses starts to approach the rate of the pump pulses and/or that the amplitude of the physiological pulses is very weak (in relation to an absolute limit, or in relation to a limit given by the amplitude of the pump pulses). The adaptive filter may remain in static mode for a predetermined time period, or until released by the external process.

In a variant, a predicted signal profile of the physiological pulses (denoted "predicted physiological profile") is used as input signal to the adaptive filter 150 (instead of the predicted signal profile of the pump pulses), and the monitoring signal is formed by the estimation signal d(n) (instead of the error signal e(n)). The foregoing discussion with respect to adaptive filters is equally applicable to this variant. Various changes and modifications to the presently preferred embodiments described herein will be apparent to those skilled in the art. Such changes and modifications may be made without departing from the spirit and scope of the present invention and without diminishing its attendant advantages. It is therefore intended that such changes and modifications be covered by the appended claims. References within this text to a, an, one and first should be construed as one or more.

Some of the filtering techniques described above in relation to step 1003' and/or step 1003" may automatically be achieved by down-sampling of the pressure signal, since the desired filtering may be achieved by the anti-aliasing filter included in a down-sampling signal processing algorithm. Additionally, some of the above-described filtering techniques may also be achieved directly in hardware, e.g., in the Analog-to-Digital (A/D) conversion by choosing an appropriate sampling frequency, i.e. due to the anti-aliasing filter which is applied before sampling.

In order to improve the detection of pressure pulses, in all monitoring concepts, it is conceivable to subject the pulse wave signal to a signal enhancement process, which removes high-frequency components, before calculation of the time difference. Such a signal enhancement process may involve subjecting the pulse wave signal to a low-pass filtering. However, a more significant improvement in SNR of the monitoring signal may be achieved by averaging several consecutive pressure pulses in the pulse wave signal, based on a predicted timing of the pressure pulses. Such a signal enhancement process would thus involve using the predicted timing to identify a set of pulse segments in the pulse wave signal, aligning the pulse segments in the time domain based on the predicted timing, and generating an average representation by summing the aligned signal values for each time value in the time domain. Optionally, the average representation is normalized by the number of pulse segments to generate a true average. The average representation may then be used as the monitoring signal. In an alternative, the average representation is generated by taking the median of the aligned signal values for each time value in the time domain. The skilled person realizes that there are further equivalent ways to process the aligned signal values to achieve a signal enhancement. In a variant, the above-described signal enhancement process may involve using the predicted timing to identify and average pulse segments from pulse wave signals acquired from plural pulse wave sensors. Thus, the monitoring signal may be generated based on plural time windows in a pulse wave signal from a single pulse wave sensor and/or from one or more time windows in pulse wave signals from different pulse wave sensors.

A corresponding signal enhancement process may be operated on the pulse generation signal.

In contrast to the first and second monitoring concepts, the third monitoring concept aims at detecting pump pulses, and if required eliminate/suppress physiological pulses that may interfere with the detection. Thus, certain embodiments described in Sections IV, V and VI may need modified before being applied for signal analysis of the pulse generation signal or the pulse wave signal obtained in the third monitoring concept. For example, the removal of physiological pulses may be carried out by any of the techniques discussed in Section IV in relation to step 1003" ("Isolating pressure data from a physiological phenomenon").

The above-mentioned pressure sensors may be of any conceivable type, e.g.

operating by resistive, capacitive, inductive, magnetic, acoustic or optical sensing, and using one or more diaphragms, bellows, Bourdon tubes, piezo-electrical components, semiconductor components, strain gauges, resonant wires, accelerometers, etc.

The extracorporeal circuit may include any type of pumping device, not only rotary peristaltic pumps as disclosed above, but also other types of positive displacement pumps, such as linear peristaltic pumps, diaphragm pumps, as well as centrifugal pumps.

The embodiments of the invention are applicable to all types of extracorporeal blood flow circuits in which blood is taken from the systemic blood circuit of the patient to have a process applied to it before it is returned to the patient. Such blood flow circuits include circuits for hemodialysis, hemofiltration, hemodiafiltration, plasmapheresis, apheresis, extracorporeal membrane oxygenation, assisted blood circulation, extracorporeal liver support/dialysis, and blood fraction separation (e.g. cells) of donor blood. The inventive technique is likewise applicable for monitoring in other types of extracorporeal blood flow circuits, such as circuits for blood transfusion, infusion, as well as heart- lung-machines.

The above-described monitoring method may be executed by a monitoring device (cf. 25 in Fig. 1), which may be implemented by special-purpose software (or firmware) run on one or more general-purpose or special-purpose computing devices. In this context, it is to be understood that each "element" or "means" of such a computing device refers to a conceptual equivalent of a method step; there is not always a one-to-one correspondence between elements/means and particular pieces of hardware or software routines. One piece of hardware sometimes comprises different means/elements. For example, a processing unit serves as one element/means when executing one instruction, but serves as another element/means when executing another instruction. In addition, one element/means may be implemented by one instruction in some cases, but by a plurality of instructions in some other cases. Such a software controlled computing device may include one or more processing units, e.g. a CPU ("Central Processing Unit"), a DSP ("Digital Signal

Processor"), an ASIC ("Application-Specific Integrated Circuit"), discrete analog and/or digital components, or some other programmable logical device, such as an FPGA ("Field Programmable Gate Array"). The monitoring device may further include a system memory and a system bus that couples various system components including the system memory to the processing unit. The system bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory may include computer storage media in the form of volatile and/or non- volatile memory such as read only memory (ROM), random access memory (RAM) and flash memory. The special-purpose software, and the adjustment factors, may be stored in the system memory, or on other removable/non- removable volatile/non-volatile computer storage media which is included in or accessible to the computing device, such as magnetic media, optical media, flash memory cards, digital tape, solid state RAM, solid state ROM, etc. The monitoring device may include one or more communication interfaces, such as a serial interface, a parallel interface, a USB interface, a wireless interface, a network adapter, etc, as well as one or more data acquisition devices, such as an A/D converter. The special-purpose software may be provided to the control device on any suitable computer-readable medium, including a record medium, a read-only memory, or an electrical carrier signal.

It is also conceivable that some (or all) method steps are fully or partially

implemented by dedicated hardware, such as an FPGA, an ASIC, or an assembly of discrete electronic components (resistors, capacitors, operational amplifier, transistors, filters, etc), as is well-known in the art.

In the following, a set of items are recited to summarize some aspects and

embodiments of the invention as disclosed in the foregoing.

Item 1. A device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the device comprises an input (28) configured to obtain a combination of signals, wherein the combination of signals is either of: a first signal from a first pulse sensor (40) arranged to detect pressure waves in the extracorporeal blood flow circuit (20) and a second signal indicative of an activation of a pulse generator (30) associated with the subject; and a first signal from a first pulse sensor (40) arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator (30) associated with the extracorporeal blood flow circuit (20);

wherein the device further comprises a signal processor (29) configured to: process the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and calculate the blood pressure value based on the time difference (ΔΤ).

Item 2. The device of item 1, wherein the first time point corresponds to a detection by the pulse sensor (40) of a pressure wave caused by the activation of the pulse generator (30), and the second time point is indicative of the activation of the pulse generator (30).

Item 3. The device of item 1 or 2, wherein the second signal originates from a second pulse sensor (35) which is arranged to detect an electrical signal resulting from the activation of the pulse generator (30). Item 4. The device of item 3, wherein the pulse generator (30) is the subject's heart, and wherein the second pulse sensor (35) is arranged in the extracorporeal blood flow circuit (20) to detect the electrical signal when conducted from the subject to the extracorporeal blood flow circuit (20) via at least one access device (1, 14) which is arranged to establish fluid communication between the extracorporeal blood flow circuit (20) and the vascular system.

Item 5. The device of item 1 or 2, wherein the second signal represents a control signal for the pulse generator (30).

Item 6. The device of item 1 or 2, wherein the second signal originates from a second pulse sensor (35) which is arranged to detect pressure waves caused by the activation of the pulse generator (30).

Item 7. The device of item 3, 4 or 6, wherein the second pulse sensor (35) is associated with an inflatable cuff (70) for attachment to the subject.

Item 8. The device of item 1, 2, 5 or 6, wherein the first pulse sensor (40), when arranged to detect pressure waves in the vascular system, is associated with an inflatable cuff (70) for attachment to the subject.

Item 9. The device of any preceding item, wherein the first signal comprises at least one first pulse and the second signal comprises at least one second pulse, and wherein the time difference (ΔΤ) is given by the first and second pulses.

Item 10. The device of item 9, wherein the signal processor (29) is further configured to determine the first time point based on said at least one first pulse, and to determine the second time point based on said at least second pulse.

Item 11. The device of item 9, wherein the signal processor (29) is configured to calculate a convolution parameter by convolving a first signal segment in the first signal with a second signal segment in the second signal, and to determine the time difference (ΔΤ) based on the convolution parameter.

Item 12. The device of item 11, wherein the convolution comprises a cross- correlation, and the convolution parameter comprises a maximum correlation coefficient resulting from the cross-correlation.

Item 13. The device of item 11 or 12, wherein the first pulse segment comprises a plurality of first pulses, and the second pulse segment comprises a plurality of second pulses.

Item 14. The device of any preceding item, wherein the signal processor (29) is configured obtain the first signal by acquiring a measurement signal from the first pulse sensor (40), which measurement signal comprises at least one first pulse representing a pressure wave generated by the pulse generator (30) and at least one interference pulse, and by processing the measurement signal to essentially eliminate said at least one interference pulse. Item 15. The device of item 14, wherein the signal processor (29) is configured to obtain a pulse profile (u(n)) which is a predicted temporal signal profile of the interference pulse, and to filter the measurement signal in the time domain, using the pulse profile (u(n)), to essentially eliminate the interference pulse while retaining the first pulse.

Item 16. The device of item 15, wherein the signal processor (29) is configured to subtract the pulse profile (u(n)) from the measurement signal.

Item 17. The device of item 16, wherein the signal processor (29) is configured to, before subtracting the pulse profile, adjust at least one of the amplitude, the time scale and the phase of the pulse profile (u(n)) with respect to the measurement signal.

Item 18. The device of item 17, wherein the signal processor (29) is configured to minimize a difference between the pulse profile (u(n)) and the measurement signal.

Item 19. The device of any one of items 17- 18, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), and wherein the signal processor (29) is configured to subtract the pulse profile (u(n)) by adjusting a phase of the pulse profile (u(n)) in relation to the measurement signal, wherein said phase is indicated by phase information obtained from at least one of: a pump rate sensor (26) coupled to said at least one pumping device (3), and a control unit (23) for said at least one pumping device (3).

Item 20. The device of item 15, wherein the signal processor (29) comprises an adaptive filter (150) which is arranged to generate an estimation signal ( d(n) ), based on the pulse profile (u(n)) and an error signal (e(n)) formed as a difference between the measurement signal and the estimation signal ( d(n) ), whereby the adaptive filter (150) is arranged to essentially eliminate said at least one interference pulse in the error signal (e(n)). Further, the adaptive filter (160) may be configured to generate the estimation signal ( d (n) ) as a linear combination of M shifted pulse profiles (u(n)), and specifically the adaptive filter (160) may be configured to linearly combine M instances of the pulse profiles (u(n)), which are properly adjusted in amplitude and phase by the adaptive filter (30).

Item 21. The device of item 20, wherein the adaptive filter (150) comprises a finite impulse response filter (152) with filter coefficients that operate on the pulse profile (u(n)) to generate the estimation signal ( d(n) ), and an adaptive algorithm (154) which optimizes the filter coefficients as a function of the error signal (e(n)) and the pulse profile (u(n)).

Item 22. The device of item 20 or 21, wherein the signal processor (29) is configured to control the adaptive filter (150) to lock the filter coefficients, based on a comparison of the rate and/or amplitude of the first pulses to a limit value.

Item 23. The device of any one of items 15-22, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), and wherein the signal processor (29) is configured to, in a reference measurement, cause said at least one pumping device (3) to generate at least one interference pulse, and obtain the pulse profile (u(n)) from a reference signal generated by a reference sensor (4a-4c).

Item 24. The device of item 23, wherein the pumping device (3) is operated to generate a sequence of interference pulses during the reference measurement, and wherein the pulse profile (u(n)) is obtained by identifying and averaging a set of interference pulses in the reference signal.

Item 25. The device of item 23 or 24, wherein the signal processor (29) is configured to effect the reference measurement intermittently during operation of the extracorporeal blood flow circuit (20) to update the pulse profile (u(n)).

Item 26. The device of any one of items 15-22, wherein the signal processor (29) is configured to obtain the pulse profile (u(n)) based on a predetermined signal profile.

Item 27. The device of item 26, wherein the signal processor (29) is configured to modify the predetermined signal profile according to a mathematical model based on a current value of one or more system parameters of the extracorporeal blood circuit (20).

Item 28. The device of any one of items 15-22, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), and wherein the signal processor (29) is configured to obtain a current value of one or more system parameters of the extracorporeal blood circuit (20), and to obtain the pulse profile (u(n)) as a function of the current value.

Item 29. The device of item 28, wherein the signal processor (29) is configured to obtain the pulse profile (u(n)) by identifying, based on the current value, one or more reference profiles (rrfn), r₂(n)) in a reference database; and obtaining the pulse profile (u(n)) based on said one or more reference profiles (rrfn), r₂(n)).

Item 30. The device of item 29, wherein said one or more system parameters is indicative of a pumping rate of said at least one pumping device (3).

Item 31. The device of item 29 or 30, wherein each reference profile (ri(n), r2(n)) in the reference database is obtained by a reference measurement in the extracorporeal blood circuit (20) for a respective value of said one or more system parameters.

Item 32. The device of item 28, wherein the signal processor (29) is configured to obtain the pulse profile (u(n)) by identifying, based on the current value, one or more combinations of energy and phase angle data in a reference database; and obtaining the pulse profile (u(n)) based on said one or more combinations of energy and phase angle data.

Item 33. The device of item 32, wherein the signal processor (29) is configured to obtain the pulse profile (u(n)) by combining a set of sinusoids of different frequencies, wherein the amplitude and phase angle of each sinusoid is given by said one or more combinations of energy and phase angle data.

Item 34. The device of item 28, wherein the signal processor (29) is configured to obtain the pulse profile (u(n)) by inputting the current value into an algorithm which calculates the response of the first pulse sensor (4a-4c) based on a mathematical model of the extracorporeal blood circuit (20).

Item 35. The device of any one of items 14-34, wherein the signal processor (29) is configured obtain the first signal by deriving, based on timing information indicative of the timing of first pulses in the measurement signal, a set of signal segments in the

measurement signal, and by aligning and combining the signal segments based on the timing information.

Item 36. The device of any preceding item, wherein the pulse generator (30) is a physiological pulse generator in the subject.

Item 37. The device of any one of items 1-35, wherein the pulse generator (30) is an electromechanical pulse generator.

Item 38. The device of item 37, wherein the pulse generator (30) is attached to the subject.

Item 39. The device of any one of items 1-3 and 5-38, wherein the pulse generator (30) is included in an inflatable cuff (70) for attachment to the subject.

Item 40. The device of item 39, which is configured to control the inflation of the cuff (70), wherein the activation of the pulse generator (30) corresponds to an inflation of the cuff (70) and wherein the second signal is obtained as a control signal for causing the inflation.

Item 41. The device of any preceding item, wherein the signal processor (29) is configured to sequentially determine time differences between first and second time points in the first and second signals, and wherein the signal processor (29) is further configured to obtain, at least once while sequentially determining the time differences and via said input (28), an absolute blood pressure reading from a calibration device (70) connected to the subject, and to convert the time differences into absolute blood pressure values based on the absolute blood pressure reading.

Item 42. The device of any preceding item, wherein the first pulse sensor (40) comprises a pressure sensor (4a-4c) in the extracorporeal blood flow circuit (20).

Item 43. The device of item 37, wherein the pulse generator (30) comprises a pumping device (3) in the extracorporeal blood flow circuit (20).

Item 44. The device of any preceding item, wherein the signal processor (29) is further configured to determine a plurality of time differences (ΔΤ) during each of a plurality of operating sessions, and to calculate, for each operating session, an average time difference based on the plurality of time differences (ΔΤ), wherein each operating session involves one and the same subject which is connected to and then disconnected from the extracorporeal blood flow circuit (20), and wherein the signal processor (29) is further configured to generate an indication of the arterial status of vascular system of the subject based on a temporal change of the average time difference as a function of operating session. Item 100. A method for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the method comprises the step of obtaining a combination of signals, wherein the combination of signals is either of: a first signal from a first pulse sensor (40) arranged to detect pressure waves in the extracorporeal blood flow circuit (20) and a second signal indicative of an activation of a pulse generator (30) associated with the subject; and a first signal from a first pulse sensor (40) arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator (30) associated with the extracorporeal blood flow circuit (20);

wherein the method further comprises the step of: processing the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and calculating the blood pressure value based on the time difference (ΔΤ).

Item 101. The method of item 100, wherein the first time point corresponds to a detection by the pulse sensor (40) of a pressure wave caused by the activation of the pulse generator (30), and the second time point is indicative of the activation of the pulse generator (30).

Item 102. The method of item 100 or 101, wherein the second signal originates from a second pulse sensor (35) which detects an electrical signal resulting from the activation of the pulse generator (30).

Item 103. The method of item 102, wherein the pulse generator (30) is the subject's heart, and wherein the second pulse sensor (35) is arranged in the extracorporeal blood flow circuit (20) and detects the electrical signal when conducted from the subject to the extracorporeal blood flow circuit (20) via at least one access device (1, 14) which is arranged to establish fluid communication between the extracorporeal blood flow circuit (20) and the vascular system.

Item 104. The method of item 100 or 101, wherein the second signal represents a control signal for the pulse generator (30).

Item 105. The method of item 100 or 101, wherein the second signal originates from a second pulse sensor (35) which detects pressure waves caused by the activation of the pulse generator (30).

Item 106. The method of item 102, 103 or 105, wherein the second pulse sensor (35) is associated with an inflatable cuff (70) for attachment to the subject.

Item 107. The method of item 1, 2, 5 or 6, wherein the first pulse sensor (40), when detecting pressure waves in the vascular system, is associated with an inflatable cuff (70) for attachment to the subject. Item 108. The method of any one of items 100-107, wherein the first signal comprises at least one first pulse and the second signal comprises at least one second pulse, and wherein the time difference (ΔΤ) is given by the first and second pulses.

Item 109. The method of item 108, further comprising: determining the first time point based on said at least one first pulse, and determining the second time point based on said at least second pulse.

Item 110. The method of item 108, wherein said processing comprises: calculating a convolution parameter by convolving a first signal segment in the first signal with a second signal segment in the second signal, and determinining the time difference (ΔΤ) based on the convolution parameter.

Item 111. The method of item 110, wherein the convolution comprises a cross- correlation, and the convolution parameter comprises a maximum correlation coefficient resulting from the cross-correlation.

Item 112. The method of item 110 or 111, wherein the first pulse segment comprises a plurality of first pulses, and the second pulse segment comprises a plurality of second pulses.

Item 113. The method of any one of items 100-112, further comprising: obtaining the first signal by acquiring a measurement signal from the first pulse sensor (40), which measurement signal comprises at least one first pulse representing a pressure wave generated by the pulse generator (30) and at least one interference pulse; and processing the measurement signal to essentially eliminate said at least one interference pulse.

Item 114. The method of item 113, further comprising: obtaining a pulse profile (u(n)) which is a predicted temporal signal profile of the interference pulse; and filtering the measurement signal in the time domain, using the pulse profile (u(n)), to essentially eliminate the interference pulse while retaining the first pulse.

Item 115. The method of item 114, wherein said filtering comprises: subtracting the pulse profile (u(n)) from the measurement signal.

Item 116. The method of item 115, further comprising, before subtracting the pulse profile: adjusting at least one of the amplitude, the time scale and the phase of the pulse profile (u(n)) with respect to the measurement signal.

Item 117. The method of item 116, wherein said adjusting comprises: minimizing a difference between the pulse profile (u(n)) and the measurement signal.

Item 118. The method of any one of items 116-117, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), and wherein said subtracting the pulse profile (u(n)) comprises: obtaining phase information from at least one of: a pump rate sensor (25) coupled to said at least one pumping device (3) and a controller (24) for said at least one pumping device (3); and adjusting a phase of the pulse profile (u(n)) in relation to the measurement data based on the phase information. Item 119. The method of item 114, further comprising: operating an adaptive filter (160) to generate an estimation signal ( d(n) ), based on the pulse profile (u(n)) and an error signal (e(n)) formed as a difference between the measurement data and the estimation signal ( d (n) ), such that the adaptive filter (160) essentially eliminates said at least one interference pulse in the error signal (e(n)). The adaptive filter (160) may be operated to generate the estimation signal ( d (n) ) as a linear combination of M shifted pulse profiles (u(n)), and specifically the adaptive filter (160) may be operated to linearly combine M instances of the pulse profile (u(n)), which are properly adjusted in amplitude and phase by the adaptive filter (30).

Item 120. The method of item 119, wherein the adaptive filter (150) comprises a finite impulse response filter (152) with filter coefficients that operate on the pulse profile (u(n)) to generate the estimation signal ( d (n) ), and an adaptive algorithm (154) which optimizes the filter coefficients as a function of the error signal (e(n)) and the pulse profile (u(n)).

Item 121. The method of item 119 or 120, further comprising: controlling the adaptive filter (150) to lock the filter coefficients, based on a comparison of the rate and/or amplitude of the first pulses to a limit value.

Item 122. The method of any one of items 114-121, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), wherein said method further comprises, in a reference

measurement: causing said at least one pumping device (3) to generate at least one interference pulse; and obtaining the pulse profile (u(n)) from a reference signal generated by a reference sensor (4a-4c).

Item 123. The method of item 122, further comprising: operating the pumping device (3) to generate a sequence of interference pulses during the reference measurement, and wherein said obtaining the pulse profile (u(n)) comprises: identifying and averaging a set of interference pulses in the reference signal.

Item 124. The method of item 122 or 123, further comprising: intermittently effecting the reference measurement to update the pulse profile (u(n)) during operation of the extracorporeal blood flow circuit (20).

Item 125. The method of any one of items 114-121, wherein said pulse profile (u(n)) is obtained based on a predetermined signal profile.

Item 126. The method of item 125, further comprising: modifying the predetermined signal profile according to a mathematical model based on a current value of one or more system parameters of the extracorporeal blood circuit (20).

Item 127. The method of any one of items 114-21, wherein said at least one interference pulse originates from at least one pumping device (3) in the extracorporeal blood flow circuit (20), wherein said obtaining the pulse profile (u(n)) comprises: obtaining a current value of one or more system parameters of the extracorporeal blood circuit (20); and obtaining the pulse profile (u(n)) as a function of the current value.

Item 128. The method of item 127, wherein said obtaining the pulse profile (u(n)) comprises: identifying, based on the current value, one or more reference profiles ( i(n), r2(n)) in a reference database; and obtaining the pulse profile (u(n)) based on said one or more reference profiles (ri(n), r2(n)).

Item 129. The method of item 128, wherein said one or more system parameters is indicative of a pumping rate of said at least one pumping device (3).

Item 130. The method of item 128 or 129, wherein each reference profile (rrfn), r2(n)) in the reference database is obtained by a reference measurement in the

extracorporeal blood circuit (20) for a respective value of said one or more system parameters.

Item 131. The method of item 127, wherein said obtaining the pulse profile (u(n)) comprises: identifying, based on the current value, one or more combinations of energy and phase angle data in a reference database; and obtaining the pulse profile (u(n)) based on said one or more combinations of energy and phase angle data.

Item 132. The method of item 131, wherein wherein said obtaining the pulse profile (u(n)) comprises: combining a set of sinusoids of different frequencies, wherein the amplitude and phase angle of each sinusoid is given by said one or more combinations of energy and phase angle data.

Item 133. The method of item 127, wherein said obtaining the pulse profile (u(n)) comprises: inputting the current value into an algorithm which calculates the response of the first pulse sensor (4a-4c) based on a mathematical model of the extracorporeal blood circuit (20).

Item 134. The method of any one of items 113-133, further comprising: obtaining the first signal by deriving, based on timing information indicative of the timing of first pulses in the measurement signal, a set of signal segments in the measurement signal, and by aligning and combining the signal segments based on the timing information.

Item 135. The method of any one of items 100-134, wherein the pulse generator (30) is a physiological pulse generator in the subject.

Item 136. The method of any one of items 100-134, wherein the pulse generator (30) is an electromechanical pulse generator.

Item 137. The method of item 136, wherein the pulse generator (30) is attached to the subject.

Item 138. The method of any one of items 100-102 and 104-137, wherein the pulse generator (30) is included in an inflatable cuff (70) for attachment to the subject.

Item 139. The method of item 138, further comprising controlling the inflation of the cuff (70), wherein the activation of the pulse generator (30) corresponds to an inflation of the cuff (70), and wherein said obtaining the second signal comprises: obtaining a control signal for causing the inflation.

Item 140. The method of any one of items 100-139, further comprising: sequentially determining time differences between first and second time points in the first and second signals; obtaining, at least once while sequentially determining the time differences, an absolute blood pressure reading from a calibration device (70) connected to the subject; and converting the time differences into absolute blood pressure values based on the absolute blood pressure reading.

Item 141. The method of any one of items 100-140, wherein the first pulse sensor (40) comprises a pressure sensor (4a-4c) in the extracorporeal blood flow circuit (20).

Item 142. The method of item 136, wherein the pulse generator (30) comprises a pumping device (3) in the extracorporeal blood flow circuit (20).

Item 143. The method of any one of items 100-142, further comprising: determining a plurality of time differences (ΔΤ) during each of a plurality of operating sessions; and calculating, for each operating session, an average time difference based on the plurality of time differences (ΔΤ), wherein each operating session involves one and the same subject which is connected to and then disconnected from the extracorporeal blood flow circuit (20), and wherein the method further comprises: generating an indication of the arterial status of vascular system of the subject based on a temporal change of the average time difference as a function of operating session.

Item 200. A computer program product comprising instructions for causing a computer to perform the method of any one of items 100-143. Item 300. A device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the device comprises input means (28) for obtaining a combination of signals, wherein the combination of signals is either of: a first signal from a first pulse sensor (40) arranged to detect pressure waves in the extracorporeal blood flow circuit (20) and a second signal indicative of an activation of a pulse generator (30) associated with the subject; and a first signal from a first pulse sensor (40) arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator (30) associated with the extracorporeal blood flow circuit (20);

wherein the device further comprises: means (803) for processing the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and means (803) for calculating the blood pressure value based on the time difference (ΔΤ).

Embodiments of the device as set forth in item 300 may correspond to the embodiments of the device as set forth in items 101-143. Item 400. An apparatus for blood treatment, comprising an extracorporeal blood flow circuit (20) adapted for connection to the vascular system of a subject and operable to circulate blood from the subject through a blood processing device (6) and back to the subject, and the device as set forth in any one of items 1-44 and 300.

Item 401. The apparatus of item 400, further comprising an inflatable cuff (70) for attachment to the subject, wherein the cuff comprises at least one of a pulse generation means (71) and a sensor means (72) , wherein the cuff is operable to perform at least one of the functions: operating the pulse generation means (71) to generate pressure waves in the extracorporeal circuit (20), operating the sensor means (72) to detect pressure waves in the vascular system of the subject, and operating the sensor means (72) to detect an activation of the pulse generator (30) associated with the subject.

Claims

1. A device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the device comprises an input (28) configured to obtain a combination of signals, wherein the combination of signals is either of:

i) a first signal from a first pulse sensor (40) arranged to detect pressure waves in the extracorporeal blood flow circuit (20) and a second signal indicative of an activation of a pulse generator (30) associated with the subject; and

ii) a first signal from a first pulse sensor (40) arranged to detect pressure waves in the vascular system and a second signal indicative of an activation of a pulse generator (30) associated with the extracorporeal blood flow circuit (20);

wherein the device further comprises a signal processor (29) configured to:

process the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and

calculate the blood pressure value based on the time difference (ΔΤ).

2. The device of claim 1, wherein the first time point corresponds to a detection by the pulse sensor (40) of a pressure wave caused by the activation of the pulse generator (30), and the second time point is indicative of the activation of the pulse generator (30).

3. The device of claim 1 or 2, wherein the second signal originates from a second pulse sensor (35) which is arranged to detect an electrical signal resulting from the activation of the pulse generator (30).

4. The device of claim 3, wherein the pulse generator (30) is the subject's heart, and wherein the second pulse sensor (35) is arranged in the extracorporeal blood flow circuit

(20) to detect the electrical signal when conducted from the subject to the extracorporeal blood flow circuit (20) via at least one access device (1, 14) which is arranged to establish fluid communication between the extracorporeal blood flow circuit (20) and the vascular system.

5. The device of claim 1 or 2, wherein the second signal represents a control signal for the pulse generator (30).

6. The device of claim 1 or 2, wherein the second signal originates from a second pulse sensor (35) which is arranged to detect pressure waves caused by the activation of the pulse generator (30).

7. The device of claim 3, 4 or 6, wherein the second pulse sensor (35) is associated with an inflatable cuff (70) for attachment to the subject.

8. The device of claim 1, 2, 5 or 6, wherein the first pulse sensor (40), when arranged to detect pressure waves in the vascular system, is associated with an inflatable cuff (70) for attachment to the subject.

9. The device of any preceding claim, wherein the first signal comprises at least one first pulse and the second signal comprises at least one second pulse, and wherein the time difference (ΔΤ) is given by the first and second pulses.

10. The device of claim 9, wherein the signal processor (29) is further configured to determine the first time point based on said at least one first pulse, and to determine the second time point based on said at least second pulse.

11. The device of claim 9, wherein the signal processor (29) is configured to calculate a convolution parameter by convolving a first signal segment in the first signal with a second signal segment in the second signal, and to determine the time difference (ΔΤ) based on the convolution parameter.

12. The device of any preceding claim, wherein the signal processor (29) is configured obtain the first signal by acquiring a measurement signal from the first pulse sensor (40), which measurement signal comprises at least one first pulse representing a pressure wave generated by the pulse generator (30) and at least one interference pulse, and by processing the measurement signal to essentially eliminate said at least one interference pulse.

13. The device of claim 12, wherein the signal processor (29) is configured to obtain a pulse profile (u(n)) which is a predicted temporal signal profile of the interference pulse, and to filter the measurement signal in the time domain, using the pulse profile (u(n)), to essentially eliminate the interference pulse while retaining the first pulse.

14. The device of claim 12 or 13, wherein the signal processor (29) is configured obtain the first signal by deriving, based on timing information indicative of the timing of first pulses in the measurement signal, a set of signal segments in the measurement signal, and by aligning and combining the signal segments based on the timing information.

15. The device of any preceding claim, wherein the pulse generator (30) is a physiological pulse generator in the subject.

16. The device of any one of claims 1- 14, wherein the pulse generator (30) is an electromechanical pulse generator.

17. The device of claim 16, wherein the pulse generator (30) is attached to the subject.

18. The device of any one of claims 1-3, 5-14 and 16-17, wherein the pulse generator (30) is included in an inflatable cuff (70) for attachment to the subject.

19. The device of claim 18, which is configured to control the inflation of the cuff (70), wherein the activation of the pulse generator (30) corresponds to an inflation of the cuff (70) and wherein the second signal is obtained as a control signal for causing the inflation.

20. The device of claim 16, wherein the pulse generator (30) comprises a pumping device (3) in the extracorporeal blood flow circuit (20).

21. The device of any preceding claim, wherein the signal processor (29) is configured to sequentially determine time differences between first and second time points in the first and second signals, and wherein the signal processor (29) is further configured to obtain, at least once while sequentially determining the time differences and via said input (28), an absolute blood pressure reading from a calibration device (70) connected to the subject, and to convert the time differences into absolute blood pressure values based on the absolute blood pressure reading.

22. The device of any preceding claim, wherein the first pulse sensor (40) comprises a pressure sensor (4a-4c) in the extracorporeal blood flow circuit (20).

23. The device of any preceding claim, wherein the signal processor (29) is further configured to determine a plurality of time differences (ΔΤ) during each of a plurality of operating sessions, and to calculate, for each operating session, an average time difference based on the plurality of time differences (ΔΤ), wherein each operating session involves one and the same subject which is connected to and then disconnected from the extracorporeal blood flow circuit (20), and wherein the signal processor (29) is further configured to generate an indication of the arterial status of vascular system of the subject based on a temporal change of the average time difference as a function of operating session.

24. A device for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the device comprises input means (28) for obtaining a combination of signals, wherein the combination of signals is either of:

wherein the device further comprises:

means (803) for processing the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and

means (803) for calculating the blood pressure value based on the time difference

(ΔΤ).

25. An apparatus for blood treatment, comprising an extracorporeal blood flow circuit (20) adapted for connection to the vascular system of a subject and operable to circulate blood from the subject through a blood processing device (6) and back to the subject, and the device as set forth in any one of claims 1-24.

26. The apparatus of claim 25, further comprising an inflatable cuff (70) for attachment to the subject, wherein the cuff comprises at least one of a pulse generation means (71) and a sensor means (72) , wherein the cuff is operable to perform at least one of the functions: operating the pulse generation means (71) to generate pressure waves in the extracorporeal circuit (20), operating the sensor means (72) to detect pressure waves in the vascular system of the subject, and operating the sensor means (72) to detect an activation of the pulse generator (30) associated with the subject.

27. A method for determining a blood pressure value of a subject, wherein an extracorporeal blood flow circuit (20) is connected in fluid communication with the vascular system of the subject, wherein the method comprises the step of obtaining a combination of signals, wherein the combination of signals is either of:

wherein the method further comprises the step of:

processing the first and second signals to determine a time difference (ΔΤ) between a first time point associated with the first signal and a second time point associated with the second signal; and

calculating the blood pressure value based on the time difference (ΔΤ).

28. A computer program product comprising instructions for causing a computer to perform the method of claim 27.