US20140378795A1 - Determination of a physiological parameter - Google Patents
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- US20140378795A1 US20140378795A1 US14/480,190 US201414480190A US2014378795A1 US 20140378795 A1 US20140378795 A1 US 20140378795A1 US 201414480190 A US201414480190 A US 201414480190A US 2014378795 A1 US2014378795 A1 US 2014378795A1
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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- A—HUMAN NECESSITIES
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- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
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Abstract
Methods and systems are provided for analyzing a physiological signal by applying a continuous wavelet transform on the signal and comparing the wavelet transformation to a library of wavelet signatures corresponding to one or more physiological conditions and/or patient conditions. A pulse oximeter system may relate the wavelet transformation with one or more of the wavelet signatures based on filters and/or thresholds, and may determine that the wavelet transformation indicates that the patient of the physiological signal has a physiological condition indicated by the related wavelet signature. In some embodiments, the pulse oximeter system may use previous analyses in a neural network to update the library. Further, non-physiological components of the wavelet transformation may also be identified and removed.
Description
- This application is a continuation of U.S. patent application Ser. No. 12/856,801, entitled “Determination of a Physiological Parameter,” filed Aug. 16, 2010, which claims the benefit of U.S. Provisional Application No. 61/245,575, entitled “Determination of a Physiological Parameter,” filed Sep. 24, 2009, each of which is herein incorporated by reference in their entirety.
- The present disclosure relates generally to medical devices and, more particularly, to methods of analyzing physiological parameters using wavelet transforms.
- This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
- In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of devices have been developed for monitoring many such physiological characteristics. Such devices provide doctors and other healthcare personnel with the information they need to provide the best possible healthcare for their patients. As a result, such monitoring devices have become an indispensable part of modern medicine.
- One technique for monitoring certain physiological characteristics of a patient is commonly referred to as pulse oximetry, and the devices built based upon pulse oximetry techniques are commonly referred to as pulse oximeters. Pulse oximetry may be used to measure various blood flow characteristics, such as the blood-oxygen saturation of hemoglobin in arterial blood, the volume of individual blood pulsations supplying the tissue, and/or the rate of blood pulsations corresponding to each heartbeat of a patient. In fact, the “pulse” in pulse oximetry refers to the time varying amount of arterial blood in the tissue during each cardiac cycle.
- Pulse oximeters typically utilize a non-invasive sensor that transmits light through a patient's tissue and that photoelectrically detects the absorption of the transmitted light in such tissue. A typical pulse oximeter may use light emitting diodes (LEDs) to measure light absorption by the blood. The absorbed and/or scattered light may be detected by the pulse oximeter, which may generate a signal that is proportional to the intensity of the detected light. The received signal may be further processed, and various physiological parameters may be determined based on signal features.
- As certain signal features in the signal may be analyzed to determine physiological parameters, it may be beneficial to analyze the signal in a form from which various signal characteristics may be perceived. For example, some signal processing techniques include transforming the signal such that it may be analyzed in the frequency domain. However, such analyses may not provide information that is discernable in the time domain. Methods of processing the signal to perceive various signal characteristics in different domains, and methods of analyzing the processed signal may better enable the identification of physiological conditions based on a physiological signal.
- Advantages of the disclosed techniques may become apparent upon reading the following detailed description and upon reference to the drawings in which:
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FIG. 1 illustrates a perspective view of a pulse oximeter in accordance with an embodiment; -
FIG. 2 illustrates a simplified block diagram of a pulse oximeter, according to an embodiment; -
FIGS. 3 and 4 depict, respectively, a graph of physiological signal and a corresponding scalogram resulting from a wavelet transformation of the physiological signal, according to an embodiment; -
FIGS. 5-7 illustrate a method of comparing the wavelet transformation of a physiological signal to a wavelet transformation of a signal corresponding to a physiological condition, according an embodiment; and -
FIG. 8 is a flow chart of a method for determining a physiological condition based on a wavelet transformation of a physiological signal, according to an embodiment. - One or more specific embodiments of the present techniques will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
- Present embodiments relate to determining information from a patient's physiological signal based on a processing and/or comparison of the physiological signal with known signal features. More specifically, a physiological signal is generated by a physiological monitoring system, such as pulse oximeter, in response to light that is detected after being emitted and transmitted through the patient's tissue. The physiological signal may be processed using wavelet transforms to determine various physiological parameters. A wavelet transformation of the physiological signal may enable analyses of certain signal characteristics with respect to both time and frequency. While certain processing techniques or transformations of the physiological signal may result in a globally averaged energy value without information regarding the temporal components of the signal, continuous wavelet transformations may produce information regarding characteristics of the physiological signal (e.g., frequency and/or amplitude) with regard to temporal locations.
- The wavelet transformation of the physiological signal, or the wavelet transformed product, may be analyzed for known signal features by comparing the wavelet transformed product to a library of wavelet signatures. Each of the wavelet signatures in the library may correspond to one or more physiological conditions and/or patient characteristics. Signal processing techniques may be utilized to determine whether the wavelet transformed product shares substantially similar characteristics with the wavelet signatures, which may indicate that the patient has the physiological condition correlated with the similar wavelet signature. As wavelet transformed products may enable analyses of multiple dimensions of signal characteristics (e.g., frequency and amplitude with respect to time), comparisons between the wavelet transformed products and the wavelet signatures may also include comparisons of multiple dimensions of signal characteristics. Such multi-dimension analyses and/or comparisons may provide more information for estimating physiological data from the physiological signal, and may enable a more accurate estimation than a comparison of signal characteristics in one dimension. In some embodiments, the probability of a physiological condition may also be produced to indicate the accuracy of the physiological data estimates.
- Furthermore, analyses based on wavelet transforms may also involve a supervised learning technique, such as a neural network. For example, previous determinations of physiological conditions may be used to determine physiological conditions in future analyses. In one embodiment, an analysis of a previous wavelet transformed product may have categorized the previous wavelet transformed product as displaying signal characteristics corresponding to a particular physiological condition. Data from the analyses of the previous wavelet transformed product may be used to update the library of wavelet signatures, and/or may be used to form filters for identifying one or more current physiological conditions in analyses of future wavelet transformed products.
- Analyzing the wavelet transformed product may also be useful in identifying components in the physiological signal which may be non-physiological and may affect the accuracy of physiological data determined from the signal. For example, various types of noise and interference that may affect the accuracy physiological data estimations may include non-physiological noise sources such as electrical noise, patient motion, or other interferences. Certain non-physiological signal components may be identifiable when amplitude and/or frequency characteristics are analyzed with regard to temporal locations. In some embodiments, the non-physiological signal components may also be removed after they are identified, such that calculations and/or comparisons may be performed on a “clean” wavelet transformed product, improving the likelihood of estimating accurate physiological data.
- Turning to
FIG. 1 , a perspective view of a medical device is illustrated in accordance with an embodiment. The medical device may be apulse oximeter system 100. Thepulse oximeter system 100 may include amonitor 102, such as those available from Nellcor Puritan Bennett LLC. Thepulse oximeter system 100 may be utilized to observe the blood constituents of a patient's arterial blood to facilitate estimation of the state of oxygen exchange in the patient's body by emitting light into tissue and detecting the light after dispersion and/or reflection by the tissue. The amount of light that passes through the tissue and other characteristics of the light may vary in accordance with the changing amount of certain blood constituents in the tissue and the related light absorption and/or scattering. As with conventional pulse oximeter systems, thepulse oximeter system 100 may emit light from two or more LEDs or lasers into pulsatile tissue and then detect the transmitted light with a light detector (e.g., a photodiode or photo-detector) after the light has passed through the pulsatile tissue. Such measurements may be utilized to estimate a percentage of blood oxygen saturation in the probed volume of blood. - The
monitor 102 may be configured to display calculated parameters on adisplay 104. As illustrated inFIG. 1 , thedisplay 104 may be integrated into themonitor 102. However, themonitor 102 may also be configured to provide data via a port to an external display or secondary monitor. Thedisplay 104 may be configured to display computed physiological data including, for example, an oxygen saturation percentage, a pulse rate, and/or aplethysmographic waveform 106. The oxygen saturation percentage may be a functional arterial hemoglobin oxygen saturation measurement in units of percentage SpO2, while the pulse rate may indicate a patient's pulse rate in beats per minute. Themonitor 102 may also display information related to alarms, monitor settings, and/or signal quality via indicator lights 108. - To facilitate user input, the
monitor 102 may include a plurality ofcontrol inputs 110. Thecontrol inputs 110 may include fixed function keys, programmable function keys, and soft keys. Specifically, thecontrol inputs 110 may correspond to soft key icons in thedisplay 104. Pressingcontrol inputs 110 associated with, or adjacent to, an icon in the display may select a corresponding option. Themonitor 102 may also include acasing 111. Thecasing 111 may aid in the protection of the internal elements of themonitor 102 from damage. - The
monitor 102 may further include asensor port 112. Thesensor port 112 may allow for connection to anexternal sensor 114, via acable 115 which connects to thesensor port 112. Thesensor 114 may be of a disposable or a non-disposable type. Furthermore, thesensor 114 may obtain readings from a patient, which can be used by the monitor to calculate certain physiological characteristics such as the blood-oxygen saturation of hemoglobin in arterial blood, the volume of individual blood pulsations supplying the tissue, and/or the rate of blood pulsations corresponding to each heartbeat of a patient. - Turning to
FIG. 2 , a simplified block diagram of apulse oximeter system 100 is illustrated in accordance with an embodiment. Specifically, certain components of thesensor 114 and themonitor 102 are illustrated inFIG. 2 . Thesensor 114 may include anemitter 116, adetector 118, and anencoder 120. Theemitter 116 may receive modulated drive signals from themonitor 102, and may activate and deactivate a light emitting device at certain intervals. For example, themonitor 102 may activate and deactivate components that emit light of different wavelengths, such that light of different wavelength is alternately emitted. - The
emitter 116 may be capable of emitting one or more wavelengths of light, e.g., RED and infrared (IR) light, into the tissue of apatient 117, where the RED wavelength may be between about 600 nm and about 700 nm, and the IR wavelength may be between about 800 nm and about 1000 nm. Theemitter 116 may include a single emitting device, for example, with two light emitting diodes (LEDs) or theemitter 116 may include a plurality of emitting devices with, for example, multiple LED's at various locations. Regardless of the number of light emitting devices, theemitter 116 may be used to measure, for example, blood oxygen saturation, water fractions, hematocrit, or other physiologic parameters of thepatient 117, as discussed herein. It should be understood that, as used herein, the term “light” may refer to one or more of radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation, and may also include any wavelength within the radio, microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any suitable wavelength of light may be appropriate for use in accordance with the present disclosure. - In one embodiment, the
detector 118 may be an array of detector elements that may be capable of detecting light at various intensities and wavelengths. In operation, light enters thedetector 118 after passing through the tissue of thepatient 117. Thedetector 118 may convert the light at a given intensity, which may be directly related to the absorbance and/or reflectance of light in the tissue of thepatient 117, into an electrical signal. That is, when more light at a certain wavelength is absorbed or reflected, less light of that wavelength is typically received from the tissue by thedetector 118. For example, thedetector 118 may include one or more photodiodes, or any other element capable of converting light into either a current or voltage. After converting the received light to an electrical signal, thedetector 118 may send the signal, which may be a pleth signal, to themonitor 102, where physiological characteristics may be calculated based at least in part on the absorption of light in the tissue of thepatient 117. - In some embodiments, the
sensor 114 may include anencoder 120, which may contain information about thesensor 114, such as what type of sensor it is (e.g., whether the sensor is intended for placement on a forehead or digit) and the wavelengths of light emitted by theemitter 116. This information may allow themonitor 102 to select appropriate algorithms and/or calibration coefficients for calculating the patient's 117 physiological characteristics. Theencoder 120 may, for instance, be a memory on which one or more of the following information may be stored for communication to the monitor 102: the type of thesensor 114; the wavelengths of light emitted by theemitter 116; and the proper calibration coefficients and/or algorithms to be used for calculating the patient's 117 physiological characteristics. In one embodiment, the data or signal from theencoder 120 may be decoded by a detector/decoder 121 in themonitor 102. - Signals from the
detector 118 and theencoder 120 may be transmitted to themonitor 102. Themonitor 102 may include one ormore processors 122 coupled to aninternal bus 124. Also connected to thebus 124 may be aRAM memory 126 and adisplay 104. Themonitor 102 may also include amodulator 132, which may include a time processing unit (TPU) 128 andlight drive circuitry 130. Themodulator 132 may modulate the drive signals that activate the LEDs or other emitting structures of theemitter 116. Themodulator 132 may be hardware-based, software-based, or some combination thereof. For example, a software aspect of themodulator 132 may be stored on thememory 126 and may be controlled by theprocessor 122. TheTPU 128 may include a sine wave generator, and may provide timing control signals tolight drive circuitry 130, which controls when theemitter 116 is activated, and if multiple light sources are used, the multiplexed timing for the different light sources.TPU 128 may also control the gating-in of signals fromdetector 118 through aswitching circuit 134. These signals are sampled at the proper time, depending at least in part upon which of multiple light sources is activated, if multiple light sources are used. - The received signal from the
detector 118 may be processed to provide certain physiological data. In one embodiment, the received signal may be passed through anamplifier 136, a low pass filter 138, and an analog-to-digital converter (ADC) 140 for amplifying, filtering, and digitizing the electrical signals the from thesensor 114. The digital data may then be stored in a queued serial module (QSM) 142, for later downloading to RAM 126 asQSM 142 fills up. There may also be multiple parallel paths for separate amplifiers, filters, and A/D converters for multiple light wavelengths or spectra received. Further, theprocessor 122 may calculate the oxygen saturation based on the received signals corresponding to the light received by thedetector 118. For example, theprocessor 122 may perform instructions or algorithms stored on thememory 144, and may be configured to perform calculations to estimate physiological parameters based on the received signals. - The
processor 122 may also be configured to perform various signal processing operations (e.g., filtering, warping, Fourier transforms, and/or wavelet transforms, etc.) in the estimation of physiological parameters. For example, theprocessor 122 may apply a wavelet transform to the physiological signal, and the wavelet transformed product may be analyzed to identify a physiological condition and/or identify non-physiological signal components. In some embodiments, the wavelet transformed product may be compared with other wavelet transformations which correspond to various physiological conditions. A table of wavelet transformations may be stored in theRAM 126 ormemory 144. Furthermore, theprocessor 122 may utilize a supervised learning technique, such as a neural network, to identify physiological conditions in a physiological signal. - The
graph 150 ofFIG. 3 , depicts theamplitude 152 overtime 154 of aphysiological signal 156 which may be generated by amonitor 102 in response to light received at the detector 118 (as inFIG. 2 ). Thephysiological signal 156 may be any signal from which may be processed according to the present techniques, and may include information obtainable by emitting various types of waves towards thepatient 117, including photon density waves. For example, thephysiological signal 156 may include a plethysmographic (pleth) signal or phase change signal (e.g., from a photon density wave). - The
physiological signal 156 may be processed to enable the identification of certain physiological parameters of thepatient 117. In one embodiment, wavelet transforms may be applied at themonitor 102 to produce an energy map having both time and frequency information. In one embodiment, algorithms or instructions may be implemented or performed by the monitor 102 (e.g., by the processor 122) to transform thephysiological signal 156, such that the signals may be analyzed with respect to time, frequency, and/or amplitude. For example, the wavelet transform of a signal x(t) may be defined in the equation below: -
- In eq. (1), ψ′(t) is the complex conjugate of the wavelet function ψ(t). The variable a is the dilation parameter of the wavelet, and b is the location parameter of the wavelet. In one or more embodiments, any suitable wavelet function, including a Morlet wavelet, may be used to obtain a time-frequency representation of the
physiological signal 156. The transform of eq. (1) may be regarded as a time-frequency representation where the characteristic frequency associated with the wavelet is inversely proportional to the scale a, and can be used to construct a representation of a signal on a transform surface. The energy density function of the wavelet transform, also referred to as the scalogram, may be defined by the equation below: -
- where “| |” is the modulus operator. Thus, by applying the wavelet transform on a time-based signal for the time-frequency representation of the signal, and then applying the energy density function of the wavelet transform, a scalogram may be produced. The scalogram, which may also be interpreted as a spectral density of frequency over time, may be a three dimensional model (having time, frequency, and magnitude) from which certain physiological information may be obtained.
- A wavelet transformation of the
physiological signal 156 inFIG. 3 is represented as thescalogram 160 inFIG. 4 . As discussed, thescalogram 160 may be an example of a wavelet transformed product, and may be a time-frequency representation of thephysiological signal 156. Thephysiological signal 156 and the time-frequency representation of thesignal 156 may be represented over thesame time 154 in thegraph 150 and thescalogram 160. Thescalogram 160 may provide a relationship betweenfrequency 162 and amplitude, which may be depicted as aspectral density 164 in thescalogram 160. For example, aspectral range 164 may be used to determine the signal amplitude at anytemporal location 154. - Different features may be seen at different frequencies of the
scalogram 160, and may match temporally with the originalphysiological signal 156. For example, changes in amplitude or frequency in thephysiological signal 156, such as the amplitude peaks 158, may correspond to certain spectral variations, such as thefeatures 166, in thespectral density 164 of thescalogram 160. The amplitude peaks 158 and/or features 166 in thescalogram 160 may correspond to some physiological parameter (e.g., oxygen saturation, pulse rate, breathing rate, etc.) within a frequency band of the wavelet-transformedsignal 156. -
Certain features 166 in thescalogram 160, such as patterns, ridges, or spots, may be the locus of points of local maxima in the plane, and may provide information such as amplitude or instantaneous frequency of thesignal 156 at thattemporal location 154. Thus, both the amplitude (i.e., represented by the spectral density 164), as well as theinstantaneous frequency 162 of thephysiological signal 156, may be available for anytemporal location 154 of thescalogram 160. As some transformations (e.g., a typical Fourier transformation) may return a globally averaged energy value without information regarding the temporal location of signal components, the temporal location of certain signal characteristics may not be available. Thus, applying wavelet transforms may be particularly useful in identifying certain signal features within thephysiological signal 156. The identification of certain signal features of thephysiological signal 156 through itscorresponding scalogram 160 may enable themonitor 102 to better determine various physiological conditions. - Due to the repetitive nature of physiological conditions, a wavelet analysis of a physiological signal enabling analyses with respect for temporal location may used to detect repetition or patterns in a scalogram. Thus, various physiological conditions may be determined by analyzing certain repeated features (e.g., patterns of ridges, spots, etc.). In contrast, noise, motion artifacts, and other non-repetitive phenomena which are not typically characterized by a recurring pattern or signature may be identified as a non-physiological signal component. Thus, various non-physiological components, such as spectral peaks or spikes, may also be identified and resolved (e.g., disregarded, removed, etc.) to produce more accurate data estimates from the physiological signal.
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FIGS. 5-7 depict one example of how a pattern indicative of a physiological condition may be detected in a scalogram. In one embodiment, a physiological signal may be processed, such as by applying wavelet transforms, to produce a wavelet transformed product, represented by thescalogram 168 inFIG. 5 . Thescalogram 168 may be analyzed in view of a wavelet signature 170 (FIG. 6 ), which may be a wavelet transform or some portion thereof, to determine the presence of some pattern of interest exemplified by thewavelet signature 170. For example, in some embodiments, thescalogram 168 may be cross-correlated with thewavelet signature 170 to determine whether the pattern of interest is present in thescalogram 168. The resulting image 172 (FIG. 7 ) may represent a cross-correlation of thescalogram 168 with thewavelet signature 170. The identification of some pattern of interest in thescalogram 168 may include setting a threshold spectral intensity for the resultingimage 172. Instances in theimage 172 that exceed a threshold intensity may indicate that the pattern from thewavelet signature 170 is present in thespectrogram 168. For example, the presence of the depicteddarker spot 174 in theimage 172 may be used to determine that a pattern from thesignature 170 exists in thescalogram 168. Detecting pattern in thescalogram 168 may indicate that the patient of thescalogram 168 has one or more physiological conditions. - The present techniques include analyzing a wavelet transformed physiological signal (e.g., a scalogram) with multiple wavelet signatures, such as the
wavelet signature 170 ofFIG. 6 . Each wavelet signature may be a wavelet transformation of a signal corresponding to a physiological condition. For example, in some embodiments, multiple wavelet signatures may be organized in a library of wavelet signatures and stored in theRAM 126 or theROM 144 of the monitor 102 (as inFIG. 2 ). Themonitor 102 may utilize various matching or filtering techniques to determine whether a wavelet transformed product shares substantially similar characteristics to any of the wavelet signatures in the library. For example, cross-correlation techniques and/or threshold techniques similar to those described with respect toFIGS. 5-7 may be used for each wavelet signature analysis. When amonitor 102 determines that a wavelet transformed product has a high correlation with a particular wavelet signature, the monitor may categorize the patient 117 from which the signal was generated as having a particular physiological condition. - Furthermore, the
monitor 102 may execute a supervised learning technique, such as an artificial neural network, to determine physiological data from a wavelet transformed product. An artificial neural network may be used to represent or process nonlinear functions, such as a wavelet transformed product, and apply and/or analyze the wavelet transformed product with a large data set, such as a library of wavelet signatures. The artificial neural network engine may be data processing architecture which may be implemented in software, hardware, or a combination of both. Further, the artificial neural network may be executed by theprocessor 122, an external computer system coupled to themonitor 102, or by hardware of the monitor 102 (not illustrated). In some embodiments, a supervised learning technique may involve utilizing a previously analyzed wavelet transformed physiological signal to update a library of wavelet signatures. - Determining a physiological condition based on a wavelet transformation of a physiological signal and a comparison of the wavelet transformed signal with multiple wavelet signatures may be depicted in the
flow chart 180 ofFIG. 8 . As discussed, aphysiological signal 182 may be generated in response to an intensity of light received at adetector 118 of a pulse oximeter sensor 114 (as inFIGS. 1 and 2 ). Themonitor 102 may process thephysiological signal 182 by applying wavelet transforms (block 184) to produce a wavelet transformedproduct 186 that is a representation of thephysiological signal 182 in time and frequency domains. For example, the wavelet transformedproduct 186 may be represented in a scalogram (as inFIGS. 4 and 5 ). Themonitor 102 may then analyze the wavelet transformedproduct 186 to determine physiological data. - The
monitor 102 may analyze the wavelet transformedproduct 186 by comparing theproduct 186 with alibrary 190 of wavelet signatures 170 (block 188). Each of thewavelet signatures 170 may be a wavelet transformation having features and/or patterns which correspond to a particular physiological condition. Furthermore, thewavelet signatures 170 may be categorized in thelibrary 190 based on patient characteristics such as gender, age, weight, or diagnosis, or characteristics of thepulse oximeter 100, including sensor type, sensor placement, etc. The categorization of wavelet signatures may enable themonitor 102 to compare the wavelet transformedproduct 186 with the most relevant wavelet signatures in thelibrary 190. For example, the monitor may determine which of thewavelet signatures 170 in thelibrary 190 have similar patient characteristics as the patient and similar sensor specifications as the sensor measuring thephysiological signal 182. Themonitor 102 may then determine whether the any of thesewavelet signatures 170 share substantially similar characteristics (e.g., patterns of scalogram features such as ridges or spots) with the wavelet transformedproduct 186. While in some embodiments, themonitor 102 may perform a comparison of the wavelet transformedproduct 186 with all thewavelet signatures 170 in thelibrary 190, in other embodiments, limiting the comparisons to categories ofsimilar wavelet signatures 170 may reduce processing power. - The comparison of the wavelet transformed
product 186 and the wavelet signatures 170 (block 188) may be performed through cross-correlating the wavelet transformedproduct 186 with one ormore wavelet signatures 170, as previously discussed with respect toFIGS. 5-7 . The resulting cross-correlated images may be filtered to determine whether a matchingwavelet signature 192 is present. A matchingwavelet signature 192 may have similar characteristics as the wavelet transformedproduct 186, or may meet a threshold level of correlation. Themonitor 102 may then use the matchingwavelet signature 192 to determine the physiological condition of the patient (block 194). Themonitor 102 may determine that the patient has one or morephysiological conditions 196 corresponding to the matchingwavelet signatures 192. - In some embodiments, the
monitor 102 may also determine the probability (block 198) that thephysiological condition 196 determined for the patient is accurate. Probability may be determined, for example, based on a level of correlation or a degree of similarity between the wavelet transformedproduct 186 and the matchingsignature 192. In one embodiment, determining the probability (block 198) may also include evaluating and ranking multiplephysiological conditions 196, such that the patient may have more information on possible and/or likelyphysiological conditions 196. Theprobability 200 and/or the physiological condition(s) 196 may be output (block 202) by themonitor 102, for example, via thedisplay 104. - The display of the physiological condition(s) 196 may include physiological data, such as a patient's heart rate, oxygen saturation, or any other monitored statistic or data. The display of physiological condition(s) 196 may also include recommendations regarding a condition of the patient or the measurement of data. For example, the recommendations may include alerts to a caregiver, such as if the patient's diagnoses requires immediate attention. Recommendations may also be displayed to improve the quality of physiological data. For example, if the position of the sensor or the movement of the patient is causing an inaccurate measurement of the
physiological signal 182, thus resulting in alow probability 200, themonitor 102 may display recommendations on how to improve theprobability 200 and the accuracy of estimatingphysiological conditions 196. Such diagnoses and/or recommendations may be organized on thedisplay 104 based on the likelihood that the diagnoses or recommendations are accurate. Furthermore, in some embodiments, the display of physiological condition(s) may also include a diagnosis, such as a specific condition or disease (e.g., arrhythmia), as well as aprobability 200 of the accuracy of the diagnosis. - As discussed, supervised learning techniques may also be implemented in accordance with the present techniques. In one embodiment, the
monitor 102 may update the library 190 (block 204) after determining thephysiological condition 196 of the patient. Updating the library (block 204) may include storing data of the analysis of a current wavelet transformedproduct 186 in thelibrary 190, such that future analyses of the wavelet transformedproduct 186 may be based on an updatedlibrary 190. For example, the monitor may categorize the current wavelet transformedproduct 186 based on the patient characteristics (e.g., age, gender, diagnosis, etc.) and the determinedphysiological condition 196. Thus, in future analyses, the monitor may accesswavelet signatures 170 specific to the current patient, or specific to a patient having similar characteristics. Such techniques may result in ahigher probability 200 that themonitor 102 may estimate an accuratephysiological condition 196, as the updated wavelet transformed product may have a higher correlation with future wavelet transformed products. This may result in higher confidence diagnoses of a patient based on the patient's wavelet transformed physiological signal. - While the disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the embodiments provided herein are not intended to be limited to the particular forms disclosed. Rather, the various embodiments may cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure as defined by the following appended claims.
Claims (20)
1. A method for generating a data library, comprising:
acquiring a plurality of physiological signals associated with a physiological condition of a patient;
performing a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and
organizing the wavelet transform products so that the wavelet transform products may be searched using a current wavelet transform product derived from a current physiological signal.
2. The method, as set forth in claim 1 , wherein acquiring the plurality of physiological signals comprises acquiring the wavelet transform products.
3. The method, as set forth in claim 1 , comprising saving the wavelet transform products to the data library.
4. The method, as set forth in claim 1 , wherein the physiological condition of the patient comprises one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.
5. The method, as set forth in claim 1 , comprising organizing the wavelet transform products based on one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.
6. The method, as set forth in claim 1 , comprising acquiring the plurality of physiological signals from a pulse oximeter sensor of a pulse oximeter system.
7. The method, as set forth in claim 6 , comprising organizing the wavelet transform products based on characteristics of the pulse oximeter system.
8. The method, as set forth in claim 6 , wherein the pulse oximeter sensor comprises an encoder configured to transmit information indicative of a type of the pulse oximeter sensor to a processor that is configured to organize the wavelet transform products based on the type of the pulse oximeter sensor.
9. The method, as set forth in claim 1 , comprising utilizing a supervised learning technique to update the wavelet transform products in the data library with the current wavelet transform product.
10. The method, as set forth in claim 9 , wherein updating the wavelet transform products in the data library comprises categorizing the current wavelet transform product obtained from a patient based on one or both of the patient's age and the patient's gender so that the updated wavelet transform products may be searched using a future wavelet transform product derived from a future physiological signal.
11. A system for generating a data library, comprising:
a processor configured to:
acquire a plurality of physiological signals associated with a physiological condition of a patient;
perform a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and
organize the wavelet transform products so that the wavelet transform products may be searched using a current wavelet transform product derived from a current physiological signal; and
a memory configured to store the wavelet transform products.
12. The system, as set forth in claim 11 , wherein the processor is configured to organize the wavelet transform products based on one or more of patient physiological data, recommendations, diagnoses, and patient characteristics.
13. The system, as set forth in claim 11 , comprising a pulse oximetry sensor configured to acquire the plurality of physiological signals.
14. The system, as set forth in claim 13 , wherein the processor is configured to organize the wavelet transform products based on characteristics of the pulse oximeter system.
15. The system, as set forth in claim 13 , wherein the pulse oximeter sensor comprises an encoder configured to transmit information indicative of a type of the pulse oximeter sensor to the processor, and the processor is configured to organize the wavelet transform products based on the type of the pulse oximeter sensor.
16. The system, as set forth in claim 11 , wherein the processor is configured to utilize a supervised learning technique to update the wavelet transform products in the data library with the current wavelet transform product.
17. A method, comprising:
receiving a plurality of physiological signals associated with a plurality of physiological conditions from a medical monitoring system at a processor;
performing, via the processor, a continuous wavelet transform on each physiological signal to generate a respective wavelet transform product for each physiological signal; and
categorizing, via the processor, the wavelet transform products in a data library based on characteristics of the medical monitoring system.
18. The method of claim 17 , wherein the wavelet transform products in the data library are categorized based on a type of the medical monitoring system.
19. The method of claim 17 , comprising:
obtaining a current physiological signal using a current medical monitoring system;
deriving a current wavelet transform product from the current physiological signal; and
identifying the wavelet transform products obtained via the medical monitoring system having similar characteristics to the current medical monitoring system, and comparing the identified wavelet transform products to the current wavelet transform product to identify the identified wavelet transform products having similar characteristics to the current wavelet transform product.
20. The method of claim 17 , wherein the medical monitoring system is a pulse oximetry system.
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Also Published As
Publication number | Publication date |
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WO2011037699A3 (en) | 2011-05-19 |
US8855749B2 (en) | 2014-10-07 |
EP2480997A2 (en) | 2012-08-01 |
CA2771856A1 (en) | 2011-03-31 |
WO2011037699A2 (en) | 2011-03-31 |
US20110071376A1 (en) | 2011-03-24 |
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