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WO2013036718A1 - Détermination de l'acceptabilité de signaux physiologiques - Google Patents

Détermination de l'acceptabilité de signaux physiologiques Download PDF

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Publication number
WO2013036718A1
WO2013036718A1 PCT/US2012/054079 US2012054079W WO2013036718A1 WO 2013036718 A1 WO2013036718 A1 WO 2013036718A1 US 2012054079 W US2012054079 W US 2012054079W WO 2013036718 A1 WO2013036718 A1 WO 2013036718A1
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data
physiological
training
signal
alarm
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Gari CLIFFORD
Qiao Li
Violeta Monasterio BAZAN
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Oxford University Innovation Ltd
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Oxford University Innovation Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0826—Detecting or evaluating apnoea events
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G06F18/2111—Selection of the most significant subset of features by using evolutionary computational techniques, e.g. genetic algorithms
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G06F18/2115—Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle

Definitions

  • PhysioToolkit components of a new research resource for complex physiologic signals
  • Circulation, vol. 101, no. 23, Jun. 2000, pp. e215-220 components of a new research resource for complex physiologic signals
  • Li, Q., R. G. Mark, and G. D. Clifford. "Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator," BioMedical Engineering OnLine, 2009, 8:13 doi: 10.1186/1475-925X-8-13.
  • Li, Q., R. G. Mark, and G. D. Clifford. Robot heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.” Physiol. Meas., vol. 29, no. 1, p. 15, 2008.
  • Peng, H., F. Long, and C. Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 27, 2005, pp. 1226-1238.
  • the present disclosure relates generally to assessing biomedical signals and, more particularly, to reducing erroneous results when assessing biomedical signals.
  • a system and method for determining acceptability of physiological signals that involves monitoring a subject with one or more channels of a physiological data capture or monitoring device or devices, such as an electrocardiogram, a pulse oximeter and/or a respiration trace, and acquiring signals from such device or devices. Signal quality of the underlying data is then measured using signal quality metrics. In one ore more embodiments, signal quality metrics are calculated on each signal. Signal quality metrics are a statistical measure of the underlying noise in the signal. Physiological parameters (or other selected subject parameters) indicative of the state of the system can also be extracted from the one or more acquired signals.
  • a set of labeled data from many patients can then be used to train a machine learning algorithm, such as an Artificial Neural Network (ANN) or a Support Vector Machine (SVM), to estimate the probability that (or classify whether) the underlying signal corresponds to a particular alarm condition or underlying noise.
  • ANN Artificial Neural Network
  • SVM Support Vector Machine
  • the accuracy of the estimate or classification can then be measured.
  • subsets of features optionally using, for example, a genetic algorithm, can be selected and the accuracy of the subset features assessed.
  • the most accurate classifier can be retained based on an independent test set of data.
  • a system including: a data acquisition system configured to acquire physiological data; and a processing system coupled to the data acquisition system, the processing system being configured to receive data acquired by the data acquisition system, the processing system further being configured to estimate a probability that the acquired physiological data corresponds to either an alarm condition or an underlying noise of the acquired data.
  • the processing system can include a local interface; and a processor, memory, a user interface, and an I/O device, each coupled to the local interface.
  • the processing system can be a mobile application for a mobile device.
  • the data acquisition system and the processing system can be integrated into a single device or can reside on separate devices.
  • a method including the steps of: acquiring a physiological signal; statistically analyzing signal noise to determine a physiological signal quality; training a machine learning algorithm to estimate probability of whether the physiological signal corresponds to either an alarm condition or an underlying noise using a set of labeled data; measuring accuracy of the estimate; selecting an estimate among a plurality of estimates, that can be for example a most-accurate estimate; validating a trained machine learning algorithm using an independent test dataset; and employing a trained and validated machine learning algorithm in real-time on new data.
  • the method can also include the step of extracting physiological parameters indicative of a system state prior to training the machine learning algorithm.
  • the method can include extracting the physiological parameters followed by combining some or all extracted physiological parameters.
  • the method can include selecting a subset of features using a feature selection algorithm followed by assessing accuracy of the selected subset of features prior to training the machine learning algorithm or selecting the most accurate estimate.
  • the feature selection algorithm can be, for example, a genetic algorithm.
  • the method can be used as one or more of an apnea alarm, an electrocardiogram alarm, or a photoplethysmogram alarm.
  • FIG. 1 shows a block diagram of an embodiment of a system for determining acceptability of physiological signals.
  • FIG. 2 shows a block diagram of an embodiment of a processing system shown in FIG. 1.
  • FIG. 3 shows one embodiment of a system and a method for determining acceptability of physiological signals.
  • FIG. 4 shows example receiver operating characteristic (ROC) curves generated when using the embodiments disclosed herein.
  • FIG. 5 example sensitivity and specificity curves generated when using the embodiments disclosed herein.
  • FIG. 6 shows another example ROC curves associated with Example 2 of the disclosed embodiments.
  • FIG. 7 shows another example of sensitivity and specificity curves associated with Example 2 of the disclosed embodiments, but without arterial blood pressure features.
  • FIG. 8 shows example sensitivity and specificity curves of all variable selections described in Example 2 using a Multi-Layer Perceptron algorithm.
  • Previous methods of assessing quality and normality of signals involved a set of heuristics, such as a threshold on spectral energy density in a given frequency region, or a threshold on a moment statistical distribution.
  • parameters and thresholds are combined either in parallel or sequentially, essentially in a univariate manner.
  • prior monitoring systems may be designed to accept or reject as noise, a signal having a parameter above or below a selected single (or series of) threshold(s). Alarms are triggered when parameters of the physiological signal go beyond the set threshold(s).
  • the present disclosure addresses and overcomes the aforementioned disadvantages.
  • the present system and method does not require explicit knowledge of the patient's physiology or condition. Additionally, the disclosed system and method allows quality metrics to be recalibrated rapidly and accurately for any given recording situation or data type, so long as labeled data is provided. Rapid and efficient real time assessment of data quality and normality is facilitated in this system by rapid classification of the data by involving a simple matrix multiplication.
  • the present system and method can simultaneously combine multiple measures of signal quality and physiological variables to determine if a segment of data is usable or not, and if useable, if the segment of data represents a normal or abnormal physiological state, thereby providing an improved physiological signal monitoring system. Since measures of noise and physiology can be combined simultaneously, the present system and method can use the relationship between both the physiological parameters and between the physiological parameters and signal noise to differentiate true events from false events due to noise. It should be noted that noise will occur in both true and false events, so it is not sufficient to just measure the fact that noise is present. Rather a covariance between the noise measurements and the physiological measurements is necessary for differentiate true events from false events due to noise. The present system and method not only measures the covariance between the noise and physiological measurements, but can also learn this covariance for any given population or combination of sensors.
  • the present system and method can employ a novel machine learning approach for combining measures of underlying signal quality and physiological parameters together in a multivariate manner, which learns the intrinsic nonlinear interrelationship between the noises and the signals in a multivariate manner.
  • Output of the machine learning classifier can be a class (or probability of belonging to a class) and hence can be combined in an almost unlimited number of physiological parameters and signal quality metrics (or measures of noise) into one useful number which tells a user (or decision algorithm) whether underlying data are providing a truthful assessment of a physiological function.
  • the disclosed system and method can, for example, be used a) for determining the quality of physiological signals (such as the electrocardiogram (ECG), pulse oximetry trace, or respiratory trace, for example), b) for determining whether or not a segment of physiological data is exhibiting abnormal behavior, and c) for false alarm reduction in monitoring environments such as in the ICU.
  • ECG electrocardiogram
  • respiratory trace for example
  • c) for false alarm reduction in monitoring environments such as in the ICU.
  • FIG. 1 illustrates a system 100 wherein acceptability of physiological signals can be determined according to an embodiment of the present disclosure.
  • the system 100 generally comprises a signal/data acquisition device or system 102 and a processing device or system 104 that are coupled such that data can be acquired and sent from the data acquisition system 102 to the processing system 104.
  • the processing system 104 may comprise, for example, a hand-held device, a portable device, a computer, server, dedicated processing system, or other system, as can be appreciated.
  • the hand-held device can be, for example, a smart mobile phone.
  • the processing system 104 may include various input devices such as a keyboard, microphone, mouse, touch screen or other device, as can be appreciated.
  • the system 100 can comprise a stand-alone device or a part of a network, such as a local area network (LAN) or wide area network (WAN).
  • LAN local area network
  • WAN wide area network
  • the signal/data acquisition system 102 is configured to acquire physiological signals or data. It can include any one or more of a conventional device used to acquire or extract physiological data concerning, for example, heart rate, breathing rate, respiration rate, blood pressure, and/or oxygen saturation level of a subject.
  • Exemplary devices include the Nellcor OxiMax N-600x Pulse Oximeter, the Welch Allen CP-50 ECG recorder, the Omron R6 Blood Pressure Monitor, the Masimo Rainbow Acoustic Respiration Rate (RRa) system, the Philips IntelliVue MP90 or SureSigns VM6 portable bedside monitor and the GE Dash 3000 or the GE-Marquette Eagle 4000 Vital Signs Patient Monitor with ECG, NIBP, and oxygen saturation (Sp0 2 ).
  • the processing system 104 in particular software provided on the processing system, is configured to receive the data acquired by the signal/data acquisition system 102 and evaluate the acquired data to determine the acceptability of the physiological signals collected.
  • the signal/data acquisition system 102 and the processing system 104 are illustrated as separate components in FIG. 1 , the two components and/or one or more of their respective functionalities can be integrated into a single system or device, if desired.
  • FIG. 2 is a block diagram illustrating an architecture for the processing system
  • the processing system 104 of FIG. 2 can comprise a processor 200, memory 202, a user interface 204, and at least one I/O device 206, each of which is connected to a local interface 208.
  • the local interface 208 may be, for example, a data bus with a contra 1/address bus as can be appreciated.
  • the processor 200 can include a central processing unit (CPU) or a semiconductor-based microprocessor in the form of a microchip.
  • the memory 202 can include any one of a combination of volatile memory elements (e.g., random access memory (RAM)) and nonvolatile memory elements (e.g., hard disk, read-only memory (ROM), tape, and the like).
  • volatile memory elements e.g., random access memory (RAM)
  • nonvolatile memory elements e.g., hard disk, read-only memory (ROM), tape, and the like.
  • the user interface 204 comprises the components with which a user interacts with the processing system 104 and therefore may comprise, for example, a keyboard, mouse, and a display, such as a liquid crystal display (LCD) monitor.
  • the user interface can also comprise, for example, a touch screen that serves both input and output functions.
  • One or more I/O devices 206 are adapted to facilitate communications with other devices or systems and may include one or more communication components such as a
  • modulator/demodulator e.g., modem
  • wireless e.g., radio frequency (RF)
  • RF radio frequency
  • the memory 202 comprises various software programs including an operating system 210 and the present system 212 for determining the acceptability of the physiological signals/data acquired by the signal/data acquisition system 102.
  • the operating system 210 controls the execution of these programs as well as other programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.
  • the present system 212 for determining the acceptability of the physiological signals/data acquired by the signal/data acquisition system 102 serves to determine the quality of the physiological signals acquired, to determine whether or not a segment of physiological data is exhibiting abnormal behavior, and to reduce false alarms in monitoring environments.
  • Components stored in the memory 202 may be executable by the processor
  • executable refers to a program file that is in a form that can ultimately be run by the processor 200.
  • executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory 202 and run by the processor 200, or source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory 202 and executed by the processor 200, and the like.
  • An executable program may be stored in any portion or component of the memory 200 including, for example, random access memory, read-only memory, a hard drive, compact disk (CD), floppy disk, or other memory components.
  • the memory 202 is defined herein as both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the memory 202 may comprise, for example, RAM, ROM, hard disk drives, floppy disks accessed via an associated floppy disk drive, compact discs accessed via a compact disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory
  • the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices.
  • the ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device.
  • the processor 200 may represent multiple processors and the memory 202 may represent multiple memories that operate in parallel.
  • the local interface 208 may be an appropriate network that facilitates communication between any two of the multiple processors, between any processor and any one of the memories, or between any two of the memories, etc.
  • the processor 200 may be of electrical or optical construction, or of some other construction as can be appreciated by those with ordinary skill in the art.
  • the operating system 210 is executed to control the allocation and usage of hardware resources such as the memory, processing time and peripheral devices in the processing system 104. In this manner, the operating system 210 serves as a foundation on which applications depend, as is generally known by those with ordinary skill in the art.
  • Various programs have been described herein. Those programs can be stored on any computer-readable medium for use by or in connection with any computer-related system or method.
  • a computer- readable medium is an electronic, magnetic, optical, or other physical device or means that contains or stores a computer program for use by or in connection with a computer-related system or method.
  • Those programs can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute the instructions.
  • FIG. 3 shown is a flow chart that provides one example of a number of possible examples of the operation of a system 212 for determining acceptability of the physiological signals acquired by the signal/data acquisition system 102 according to an embodiment of the present invention.
  • the flow chart of FIG. 3 may be viewed as depicting steps of an example of a method implemented in the processor system 104 to evaluate the acquired data.
  • Functionality of the system 212 as depicted by the example flow chart of FIG. 3 may be implemented, for example, in an object-oriented design or in some other programming architecture. Assuming the functionality is implemented in an object oriented design, each block represents functionality that may be implemented in one or more methods that are encapsulated in one or more objects.
  • the system 212 may be implemented using any one of a number of programming languages such as, for example, C, C++, or other programming languages. Alternatively, the system 212 may comprise, for example, such applications as Matlab, Lab View, or any compiled code.
  • a subject is monitored with one or more channels of a physiological data capture or monitoring device or devices, such as an
  • electrocardiogram a pulse oximeter and/or a respiration trace.
  • Such devices are typical of the ICU, ambulatory monitoring, sleep studies, and ambulatory ECG (Holter) recordings for example.
  • One or more signals are acquired 310 from the device or devices. Signal quality of underlying data is then measured 320 using quality metrics. Physiological parameters (or other selected parameters) are also extracted 330 indicative of the state of the system.
  • Signal quality is measured to determine how much the underlying data can be trusted.
  • signal quality metrics are calculated on each acquired signal.
  • Signal quality metrics are a temporal, statistical or other measure of the underlying noise in the acquired signal.
  • One or more quality metrics can be applied to the acquired signal, such as Kurtosis, spectral density, and the like.
  • Kurtosis a measure of the underlying noise in the acquired signal.
  • One or more quality metrics can be applied to the acquired signal, such as Kurtosis, spectral density, and the like.
  • Kurtosis spectral density
  • the system can also measure how much each heartbeat deviates from an average template..
  • the system can measure the cross-correlation of a signal metric against an average signal metric template. A low correlation suggests low quality data.
  • the system can also measure spectral density ratios of the acquired signals. At this stage, none of the data is rejected as noisy or not noisy. None of the data is rejected based on a selected threshold. Conversely, all of the data
  • the present system also extracts 330 and calculates physiological parameters at the same time. For example, it can extract heart rate, breathing rate, respiration rate, blood pressure, and/or oxygen saturation levels and combine them. In an embodiment, at each epoch, for example, every ten seconds, one or more of the physiological parameters can be calculated. Examples of physiological parameters that can be extracted include minimum oxygen saturation, the change in oxygen saturation, minimum heart rate, the change in heart rate, minimum respiration rate and the like. Further examples of physiological parameters that can be extracted are provided in Example 2 and Tables 1 and 2 below.
  • the system thus allows several different physiological measurements of the same parameters or variables from different acquired signals.
  • the respiration rate (RR) estimated from a spectral or auto-regressive analysis of ECG measures the same thing as RR_PDR « 3 ⁇ 4 which is an estimation of respiration rate (RR) from an ECG using frequency analysis of the ECG.
  • RR EDRs ⁇ measures the same thing as RR_PDR « 3 ⁇ 4 which is an estimation of respiration rate (RR) from an ECG using frequency analysis of the ECG.
  • the system thus allows two different ways to measure the same parameter from the same signal but two using different estimation methods. The system can then learn which two extracted parameters are better in a given circumstance.
  • the results of the signal quality measurement 320 and the extraction of the physiological parameters 330 are then provided to a machine learning algorithm, for example an Artificial Neural Network (ANN) or a Support Vector Machine (SVM).
  • ANN Artificial Neural Network
  • SVM Support Vector Machine
  • a set of labeled data from patients (the larger, the better) is used to train 340 the machine learning algorithm to classify the truth of the events, for example, to estimate whether the underlying acquired signal corresponds to a particular alarm condition or underlying noise.
  • the system can classify data as true or false resulting in the classification of thousands of events.
  • This trains the machine learning algorithm to understand not just the physiology measured but also the correlation of the combination of acquired signals regarding heart rate, breathing rate, respiration rate, blood pressure, and/or oxygen saturation that gives rise to apnea in correlation with the noise in the different acquired signals.
  • the system learns that noise generally is not independent of a signal assumption. Instead, the noise is correlated with the signal assumption. As an example when one has a heart attack the person typically clutches his or her chest, causing muscle noise. The system, thus, simultaneously learns as well the covariance between different noises and the event monitored by the acquired signal. This covariance is learned without application of heuristics or thresholds. The covariance is learned on a case-by-case basis across a patient call.
  • classification of the data is rapid, involving, for example a simple matrix multiplication allowing real time assessment of data quality abnormality.
  • the system measures 350 the accuracy of the classifier by comparing the outputs of the classifier to the labels on the test data.
  • this is followed by selecting 360 subsets of features by employing a selection algorithm, such as a genetic algorithm.
  • the probability is that some of the observed data do not contribute significantly to a data analysis, are independent events that occur with equal probability for all classes of events, or are co-linear with some other features. In such cases, the features are not needed and will reduce accuracy of the classifier.
  • One exemplary embodiment of the present system is a neonatal apnea alarm system.
  • apnea of prematurity Unlike in adults, the infant does not always begin breathing again. Instead the infant slowly begins to desaturate until the blood oxygen levels reach 90% or less.
  • the conventional monitoring system creates an alarm and the medical staff then stimulates the infant into breathing again.
  • the respiration signal from the conventional system is so noisy it is of little use in detecting apnea and so any alarm it issues there forth is typically ignored.
  • the oxygen saturation alarm is also often noisy and issues false alarms as often as 90% of the time.
  • Example 1 The disclosed embodiment was used for automatic detection of apneic episodes in neonates and was tested on almost 3,000 apnea alarms from 27 patient stays. See Daly et ah, and Monasterio et al. A technique based on the disclosed machine learning algorithm, in particular the SVM, was evaluated using ICU recordings from 27 neonate available from the Multi-Parameter Intelligent Monitoring from Intensive Care II (MIMIC II) database.
  • the MIMIC II database contains physiologic wave form data from over 3500 ICU patients hospitalized at Beth Israel Deaconess Medical Center; Boston, USA.
  • Preliminary results showed a high ability to detect apneic episodes, achieving a sensitivity of 100 %, a specificity of 96%, and an accuracy of 97% in a training set composed of 820 suspected apneic episodes.
  • a sensitivity of 94%>, a specificity of 87%, and an accuracy of 89%) was achieved in a second test set composed of 803 suspected episodes.
  • Data comprised several physiological waveforms sampled at 125 Hz (2 leads of ECG, impedance
  • IP pneumogram
  • PPG pulse photoplethsmogram
  • 1 Hz numeric time series provided by bedside monitors including heart rate (HR) derived from the ECG, and peripheral Sp0 2 derived from the PPG.
  • HR heart rate
  • the investigators decided among three (3) options: (1) the desaturation is associated to an apnea, which constitutes a positive event, (2) the desaturation is caused by noise or artifacts, which constitutes a negative event, or (3) it cannot be determined whether the desaturation is associated with an apnea or not, which constitutes an unsure event.
  • Option (1) was chosen if the following conditions were fulfilled: with an interval of 300 seconds before the desaturation event (a) the HR decreases at least 10 beats per minute (bpm), (b) the minimum HR was ⁇ 1300 bpm, (c) the quality of the ECT and the PPG waveforms was high, so that one having ordinary skill would expect the waveforms to provide reliable parameter estimates, and (d) no artifacts were present.
  • Option (2) was selected if high levels of noise and/or artifacts were clearly visible in the measured signals.
  • Option 3 was chosen if the event did not meet category either (1) or (2) conditions. The two annotators agreed for 86% of the events, which were then used as the reference set of annotations for classification. This reference set was then split into training and validation subsets for SVM analysis.
  • physiological variables were computed. There were four groups of variables: variables related to Sp0 2 , HR, RR, and quality of the signals. A total of 20 variables were computed every 5 seconds for a 300-second interval before each desaturation event. Variables related to HR and Sp0 2 were derived from a Sp0 2 and HR numerical series. In each 20 second measurement window, the minimum value and a gradient of the HR and Sp0 2 series were computed. These variables were denoted as min HR, VHR, min S p 0 2 , and VS p 0 2 respectively. The gradients were computed using standard least squares regression
  • ECG-derived respiration EDR
  • EDR ECG-derived respiration
  • RR was estimated from each derived respiratory signal and from IP signal using a breathing rate extinction algorithm described in Nemati et al., which is based on work by Mason and Tarassenko, who utilized autoregressive modeling to estimate the respiratory frequency.
  • the resulting series of derived RR were denoted as RR_EDR «3 ⁇ 4 RR_EDRft3 ⁇ 4, RR_EDR G , RR PDR ⁇ , and RR IP.
  • Nemati et al. proposes a data fusion algorithm proposed by Nemati et al.
  • This method is an application of a modified Kalman filter (KF) framework for data fusion to the estimation of RR from multiple physiological sources.
  • KF Kalman filter
  • Kalman filters are employed to obtain independent RR estimates from the series of derived RR, and then the independent estimates are fused taking into account the uncertainty associated with each estimate.
  • the fusion algorithm was applied to the series of derived RR for the 300-seconds interval before each desaturation event, and the result was denoted as RR fused.
  • Nemati et al. proposed a variation of the fusion algorithm that makes use of signal quality indexes (SQI), which are explained below.
  • SQL signal quality indexes
  • SQI are incorporated in computation of individual Kalman filters and into the fusion step to obtain a more robust RR estimation.
  • the fusion algorithm was applied with SQI to the series of derived RR for the 300-seconds interval before each desaturation, and denoted the result as RR_fused3 ⁇ 4 / .
  • a minimum value and a gradient of all RR series every 15 seconds for the 300-seconds interval before each desaturation event was calculated.
  • Variables related to signal quality were computed using SQIs as follows.
  • the selected index for determining the quality of PPG, IP, and derived respiratory signals is the spectral purity, an approach proposed in Nemati et al.
  • the spectral purity of a signal is defined in Sornmo and Website, as
  • a minimum value of a variable was selected for classification. This process was repeated for all desaturation events, and a ROC curve was constructed for each variable and each window k. Subsequently, the window corresponding to a maximum area under the curve (AUC) was selected as an optimum evaluation interval for each variable. Finally, for each desaturation event a set of 20 features was created by selecting the minimum value of each variable within its optimum evaluation period.
  • the feature with the k th highest rank as computed by the mRMR algorithm was denoted as k ., and 20 subsets of features were defined as [0083]
  • SVM classification was completed.
  • two questions needed to be addressed how to select an optimal subset of features, and how to choose an appropriate kernel.
  • two options for feature and kernel selection were compared. First, an exhaustive search for feature selection with a linear kernel was combined. Next, the feature selection algorithm with a Radial Bias Function (RBF) SVM kernel. These two options are described as follows.
  • RBF Radial Bias Function
  • Exhaustive feature search plus linear SVM The first option was using a standard SVM with a linear kernel. See Chang and Lin. First, training data were normalized so that features in the training set had zero mean and unit variance, and the test data were scaled to scaling factors used for the training data. Then, an exhaustive search was conducted by training and testing the SV with all possible feature combinations to find those combinations (CK), which provided the best classification performance. Since positive and negative classes in the data were not balanced, a penalty associated with misclassification was multiplied by a factor of r for positive events, and by a factor of 1/r for negative events, with r for equal to the ratio between negative and positive events in the training set.
  • mRMR plus RBF-SVM The second option was using an RBF kernel for the SVM.
  • An RBF kernel has been found to improve classification results over a linear kernel in most cases. See Chang and Lin.
  • RBF-SBM it is necessary to estimate two defining parameters of the RBF: the capacity C and the kernel function parameter ⁇ .
  • Results of the univariate ROC analysis are presented in Table 1 , which contains an optimum evaluation window for each feature and the corresponding AUC.
  • Table 1 contains an optimum evaluation window for each feature and the corresponding AUC.
  • a positive (negative) sign in the third column indicates that values above (below) the discrimination threshold are classified as positive events.
  • Maximum AUC, 0.93, was obtained for the minimum HR within an interval of 275 seconds before the desaturation event (feature min HR at window 2).
  • Second highest AUC was obtained for the minimum gradient of HR within an interval of 245 seconds before the desaturation event (feature VHR at window 4) (Table 1).
  • Tables 5 a and 5b present the classification results obtained with the best 20 feature combinations (those with the highest accuracy in the test set), denoted as C ⁇ . .. C 20 . Not all features could be computed for every desaturation event for two reasons. First, there were intermittently missing data in all signals, and second, the appearance of successive desaturation events with less than 20 seconds between them was frequent. Columns 'positive' and 'negative' in Table 5 a and 5b show the number (percentage) of events in which all features of the corresponding combination could be comported. The highest accuracy in the training set (88.6%) was obtained with a combination of 11 features (Ci in Tables 4 and 5a). Seven out of the twenty features are included in all 20 best combinations: min HR, VHR, min RR EDR R SA, min RR IP, VRR fused, SQI PPG and SQI IP (Tables 5a and 5b).
  • a second embodiment of the present system comprises false alarm reduction in the ICU.
  • 114 signal quality and physiological parameter metrics were extracted from the ECG, blood pressure signal and pulse oximeter signal indicative of heart rate, rhythm and signal quality, and changes in these parameters.
  • Five life threatening arrhythmia alarms were studied, for which a large percentage of the alarms were false. See Tables 6 and 7.
  • Data were broken into testing and training sets again, and a SVM was trained to separate true from false alarms in according with the present disclosure.
  • a genetic algorithm was used to select the most useful parameters from the 114 signal quality and physiological parameter metrics. When blood pressure waveform was available, 56 parameters were chosen. When no blood pressure waveform was available, 27 parameters were chosen. False alarm suppression rates varied from 98% to 38% (depending on alarm) with no true alarms suppressed.
  • Example 2 In this example the disclosed system was combined with the
  • ECG, arterial blood pressure (ABP), PPG, and Sp0 2 signals to suppress false arrhythmia alarms.
  • ABP arterial blood pressure
  • PPG PPG
  • Sp0 2 Sp0 2 signals to suppress false arrhythmia alarms.
  • ABP is an invasive measurement
  • algorithms with ABP and without ABP were compared.
  • a novel PPG signal quality assessment method using a dynamic time warping algorithm See Li and Clifford 2012) and used it to suppress the false alarms, according to the frame which Aboukhalil et al. and Deshmane et al. used.
  • the multi-parameter ICU database (PhysioNet's MIMIC II database, Saeed et al. and Goldberger et al.) was used with ECG, ABP, PPG and Sp0 2 signals and expert annotated alarms were used to develop and evaluate the algorithms. Datasets were similar to those used by Deshmane et al. They included 182 cases and totaled 4107 expert annotated alarms as the gold standard. Alarm types include Asystole, EB, ET, and VT. Each alarm was specified with an availability of different channels of signals and dispatched them into to subsets. A first subset had ECG and PPG available around each alarm. A second subset had ECG, ABP, and PPG available. Table 8 shows a relative frequency of each alarm category and their associated true and false rates. Tables 9a, 9b, and 10 show a distribution of alarms in training, test, and combined sets of the first and second subset.
  • Dynamic Time Warping algorithm was developed. See Li and Clifford 2012 .
  • a PPG beat dynamic template was built based on 30 second PPG signals as described in Li and Clifford 2012, and a correlation coefficient between each PPG beat and the template was calculated.
  • Three methods were used to fit each PPG beat with the template and three SQI matrices were obtained.
  • a first matrix was a direct comparison.
  • a second matrix was a linear interpolating and re-sampling.
  • a third matrix was a dynamic time warping.
  • a fourth matrix was a clipping detection, which detected a percentage of saturation to a maximum or minimum with a beat duration. These four matrices were fused to classify each beat into excellent (E), acceptable (A), and unacceptable (U).
  • Good beat percentage (E and A) in a 17-second analysis window 13 seconds prior to alarm onset and 4 seconds after alarm) was set as an SQI of PPG.
  • PPG as a good quality signal.
  • SQI ⁇ was set strictly to 1 in order to avoid true alarm suppression.
  • the PPG signal with an SQI above SQI ⁇ was considered as a good quality signal and fed into a false alarm suppression procedure as described in Deshmane et al.
  • the Ql th then decreased gradually and also obeyed a least true alarm suppression rule.
  • the first subset of data was used to evaluate the algorithm in this step.
  • HRs and SQIs from PPG, ABP, and ECG were then estimated to suppress false alarms according to the procedure set forth in Li et al., 2008.
  • a 20-second analysis window prior to alarm onset was used to calculate the HR and SQL Seven beat-by-beat HRs were estimated.
  • HR E CG, HRA BP , HR PP G (these three were taken directly from beats interval of corresponding channel), HR E CG_ABP, HR E CG_PPG, HRABP PPG, and
  • each beat-by-beat HR was transformed into three kinds of second-by- second HRs by calculating the maximum, minimum, and mean HR from beats around each second.
  • ADB had 4 feature types, including a mean ADB of 5 top beats (ADBmean _to p5 ), a maximum of mean ADB of 5 continuous neighbor beats (ADB max means), variance (ADBvariance), and robustfit (ADBdeita) of beats in the 20-second analysis window,
  • the SQI matrices included SQI matrices from ECG (See Li et al.), ABP (See Sun et al. and Li and Clifford, 2012), and PPG (See Li and Clifford, 2012 and Deshmane).
  • a genetic algorithm was used to select optimized variables for alarm classification between true and false alarms. See Goldberg, Leardi et al., and Huang and Wang. Training set of the second subset was used to train and evaluate the algorithm. Fifty chromosomes of a population were selected. A multivariate linear regression was used as a fitness function and root mean squared error (rMSE) was used to estimate error. After each iteration, the chromosomes were sorted by the rMSE. 10 percent of the population with smallest rMSE was kept into a next iteration. A next 45 percent was selected to cross over to create a new 90 percent of the population. Twenty percent of the 90 percent of chromosomes was randomly selected to do mutation with a 2% bit mutation rate.
  • rMSE root mean squared error
  • genes of a chromosome with smallest rMSE were selected as selected variables for this run.
  • the genetic algorithm was repeated 100 runs and the selected variables were sorted based upon times of being selected to get a variable order of best selected variables for this run.
  • the genetic algorithm was repeated for 100 runs and the selected variables were sorted based upon times of being selected to get a variable order of best selected.
  • a SVM algorithm was used to classify the alarms between true (TA) and false
  • Input layer nodes were selected from 1 up to 1 14 and increased one-by-one based on output order from the genetic algorithm.
  • Hidden layer nodes were selected from 3 to 30 and the output layer node was the only node to say if it was a true or false alarm.
  • each HR and correlated SQI was used to suppress the FAs according to previous procedure.
  • the maximum SQI of these channels was selected as the selected SQI.
  • Table 12 shows a best performance, HR variable selections, and SQI th on the training set of the second subset of data.
  • HRs there was no TA suppression for all types of alarms.
  • HR PP G mean shows 94.8%> FA suppression rate with SQI threshold from 50%> to 10%).
  • HRA BP mean was selected to create a best result for ET (73.7%>) and VT (3.6%>).
  • FIG. 5 shows sensitivity and specificity curves of all variable selections. Specifically, FIG. 5 shows the sensitivity of the training subset 500, sensitivity of the test subset 501, specificity of the test subset 502, and specificity of the training subset 503. From FIG. 5, a point with 56 selected variables was selected as having maximum sensitivity and specificity. The sensitivity was 1.0 and 0.981 for the training and test dataset, respectively. The specificity was 0.3880 and 0.361 for the training and the test set, respectively. By selecting these 56 variables, the true alarm was weighted, and ROC curves for the training set were obtained. FIG.
  • FIG.7 shows sensitivity and specificity curves of variable selection without ABP features. As shown in FIG. 7, 27 selected variables result in a best specificity of 0.292 and 0.181 for training sets 703 and test sets 702, respectively, and a best sensitivity of 1 and 0.984 for the training set 700 and test set 701 respectively. Results of alarm suppression without ABP features are shown in Table 16.
  • the MLP ANN was trained by selecting input layer nodes froml to 114 and hidden layer nodes from 3 to 30, as previously described. Models were generated as previously described and used to classify the training dataset and the test dataset.
  • FIG. 8 shows sensitivity curves of test 800 and training 801 sets and specificity curves of test 802 and training 803 sets of all variable selections with hidden layer nodes of 10.
  • the presently disclosed system and method were used to detect poor quality ECGs collected in low-resource environments, in particular for intensive care monitoring.
  • the system was adapted for use on short (10 second) 12-lead ECGs.
  • Signal quality metrics used quantified spectral energy distribution, higher order moments, and inter-channel and inter-algorithm agreement.
  • Six metrics were produced for each channel, for a total of 72 features in all. These were then presented to machine learning algorithms for training on provided labeled data. Binary labels were available, indicating whether data were acceptable or unacceptable for clinical interpretation. All data in a first set (training set), and a second set (test set) were re-annotated using two independent annotators as described in Example 1.
  • a third annotator was employed for adjudication of differences between the annotations generated by the first and the second annotators. Events were then balanced and all 1000 subjects in the first data set were used to train classifiers. For this particular embodiment three classifiers were compared. Na " ive Bayes, SVM, and a MLP ANN classifiers were three chosen. The SVM and MLP provided the best classification accuracies of 99% on the first data set and 95% on the second data set.
  • a problem of vetting system and method was specifically directed to ECG quality collected by an untrained user in ambulatory scenarios.
  • the system provided realtime feedback on ECG diagnostic quality and prompted a user to make adjustments in recording data until the ECG quality is sufficient so that an automated algorithm or medical expert may be able to make a clinical diagnosis.
  • Example 3 Data were collected by project Sana and freely provided via
  • PhysioNet A dataset included 1500 ten-second recordings of standard 12-lead ECGs, age sex, weight, and other possible relevant patient information, such as a photo of electrode placement, were included. Some of the recordings were identified initially as unacceptable or acceptable. Subsequently, participants annotated their own annotations to establish a gold standard reference database of recording quality in the data.
  • Each lead was sampled at 500 Hz with 16-bit resolution.
  • the leads were recorded simultaneously for a minimum of 10 seconds by nurses, technicians, and volunteers with varying amounts of training recorded the ECGs, to simulate an intended target user.
  • ECGs collected were reviewed by a group of annotators with varying amounts of expertise in ECG analysis, in blinded fashion for grading and interpretation. Between 3 and 18 annotators, working independently, examined each ECG, assigning it a letter and a score indicating signal quality according to the following: A (0.95): excellent; B (0.85): good; C (0.75): adequate, D (0.60): poor; and F (0): unacceptable.
  • the average score ( A s) was calculated in each case and each record was assigned to one of the three following groups.
  • Group 1 (acceptable) included records with A s of > 0.70 and NF ⁇ 1, wherein NF is a number of grades that were marked as F.
  • Group 2 (indeterminate) included records with A s > 0.70 and NF > 2.
  • QRS detection was performed on each ECG channel individually using two open source QRS detectors (eplimited and wqrs) since eplimited is less sensitive to noise. See Li et ah, 2008.
  • iSQI The percentage of beats detected on each lead which were detected on all leads.
  • bSQI The percentage of beats detected by wqrs that were also detected by eplimited.
  • kSQI The fourth moment (kurtosis) of the distribution.
  • LDA Linear Discriminant Analysis attempts to find a linear combination of features that characterize or separate two or more classes. LDA is closely related to analysis of variance (ANOVA) and regression analysis, which also attempts to express one dependent variable as a linear combination of other features or measurements. However, rather than a dependent variable being a numerical quantity, LDA uses categorical variables (the class labels).
  • the Bayes optimal solution is to predict points as being from the second class if the ratio of the log-likelihoods is below some threshold Snd so that
  • Naive Bayes is a basic probabilistic classifier. For a feature vector x with D dimensions, the Na ' ive Bayes classifier is given in a problem of automatically identifying trust as
  • d l where Xd is a ⁇ i-th element of a feature vector Xd and Ck is a posterior probability of class k.
  • Ck a posterior probability of class k.
  • Ck) were chosen to be Gaussian distributions whose parameters were adjusted in a usual maximum likelihood framework (see Bishop) and is readily implemented in MATLAB. Also, prior class probability p(Ct) was set to be uniform, which is justified because classes were balanced.
  • Support Vector Machine (SVM) classification uses a principle maximum margin hyperplane and uses a "kernel trick" to transform the data into a high-dimensional feature space for linear classification.
  • SVM Support Vector Machine
  • kernel SVM which has an objective function
  • the vector xicide is a n-y n training vector from a set of N training examples
  • y n is the associated class label (-1 / +1)
  • an is a n-y n Lagrange multiplier and is subject to constraints
  • a kernel k x n ;x m was chosen to be a radial basis function kernel and training of the classifier (determining the values for a n ) was based on a Sequential Minimal
  • SMO Session Management algorithm according to Bishop, as implemented in Matlab.
  • Slack variables' trade-off parameter C was optimized by grid search within a range of 1 to 10 3 and a scale of the RBF kernel was optimized by grid searching within the range of 0.1 to 8.
  • a classifier was trained on the 6 features extracted from each of the 12 leads and a single classifier on all 72 features combined.
  • a standard three-layer feed-forward MLP was used in which input nodes were fully connected to a next hidden layer and in turn, to an output layer.
  • the output layer consisted of a single node.
  • ⁇ ( ⁇ ) is the sigmoid mapping function and ⁇ » .
  • ⁇ are the weights to
  • the training set was divided automatically into 70% training
  • Classifier training strategy for each lead implies that a suitable classifier fusion strategy must be chosen.
  • Three fusion mechanisms were considered: (a) simple averaging of classification probabilities; (b) averaging of classification log-odds; and (c) empirical density estimation.
  • pl(ci I xi) estimated by classifier (the Na ' ive Bayes Method or the ANN) for lead 1 , an average predictive class probability was performed simply by calculating
  • Pavgi I x i ) — ⁇ pl(c i I .
  • the single lead approach produces 12 classifications (one for each lead), and competition requires a single classification per 12 lead recording, the 12 classifications must be combined in some way. This can be treated as either another classification problem, and train a second classifier (with 12 inputs and one output), or an approach previously described may be used.
  • a chosen approach involved dividing a sum of scores of each individual channel by 12. An ROC curve was then plotted and an optimal threshold was calculated. An additional step was also added, to override results obtained when a flat line was detected.
  • Table 18 shows classification results of the SVM.
  • Table 19 shows classification results of the Naive Bayes method.
  • Table 20 shows classification results for the MLP.
  • Table 21 shows competition entries with accuracy of classifiers on different data and annotations.
  • Table 22 shows classifier accuracy.
  • Example 3 Challenge data (See Behar et al. 2012) and improved quality metrics.
  • 1500 10-second recordings of standard 12- lead ECGs with full diagnostic bandwith were used. Medical personnel and volunteers with varying amounts of training in ECG recording performed the ECG recordings. Similar to Example 3, the data was balanced by generating additional bad quality data from good quality records by adding noise to clean ECGs. Again data was distributed in a 2:1 ratio into two subsets, as in Example 3. Thus, there was a first data set comprised of a training set and a test set. The second dataset comprised a balanced training set and test set. Finally a third dataset was built from a MIT-BIH arrhythmia database.
  • rSQI Ratio of number of beats detected by eplimited and wqrs.
  • Example 3 By adding the rSQI and the pcaSQI, an increase in accuracy was observed. Accuracies of 97.9% and 97.1% were achieved on the CinC training and test sets, respectively. When considering all six SQIs, a 98.0% accuracy was achieved on both the training set and the test set (arrhythmia dataset).

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Abstract

Un problème lié à un signal physiologique indique souvent la présence d'un état pathologique pertinent sur le plan médical. Ainsi, le contrôle des signaux physiologiques est primordial dans le protocole de soins aux patients. L'invention concerne ainsi un système et un procédé d'évaluation de signaux physiologiques dans le but de déterminer si oui ou non un signal physiologique correspond à un état pathologique pertinent sur le plan médical. Dans un mode de réalisation de la présente invention, celle-ci concerne un système et un procédé de détermination de l'acceptabilité de signaux physiologiques impliquant le suivi d'un sujet en contrôlant un ou plusieurs canaux d'un ou de plusieurs dispositifs de capture ou de suivi de données physiologiques, tels qu'un électrocardiogramme, un sphygmo-oxymètre et/ou un tracé de la respiration, et l'acquisition de signaux à partir d'un tel ou de tels dispositifs.
PCT/US2012/054079 2011-09-08 2012-09-07 Détermination de l'acceptabilité de signaux physiologiques Ceased WO2013036718A1 (fr)

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