Classifications

• • 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
• • 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
  • A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
  • A61B5/412—Detecting or monitoring sepsis
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
  • A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• • 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
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• • 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
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient; User input means
  • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
• • 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
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • 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
• • 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
  • A61B5/08—Measuring devices for evaluating the respiratory organs
  • A61B5/085—Measuring impedance of respiratory organs or lung elasticity
  • A61B5/086—Measuring impedance of respiratory organs or lung elasticity by impedance pneumography
• • 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
  • A61B5/091—Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• • 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
  • A61B5/1455—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 using optical sensors, e.g. spectral photometrical oximeters
  • A61B5/14551—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 using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
  • G16Z99/00—Subject matter not provided for in other main groups of this subclass

Definitions

• the present invention relates generally to the field of human health monitoring, and more particularly to the use of multivariate models for analysis of measurements of biological parameters to provide residual-based assessment of human health indicators.
• SBM Similarity-Based Modeling
• An end-to-end human health monitoring solution comprised of a wearable wireless sensing device that continuously collects vital signs sensor data and transmits it (in real-time or in periodic bursts) to a base-station computer (or cellphone/PDA) for preprocessing.
• the preprocessed data is then sent to a server over the web for analysis using a kernel-based machine learning analytical method tailored for human monitoring, such as SBM.
• SBM kernel-based machine learning analytical method tailored for human monitoring, such as SBM.
• the SBM technology is trained to be specific to each individual's normal vital signs characteristics. Due to the variation in vital signs data from human to human, this capability is crucial for any human monitoring system to be effective.
• the server can be remotely located from the patient.
• the analysis performed at the server with SBM or other related kernel-based method works by generating estimates of the vital signs (i.e., physiological data) that have been determined from the sensor data. These estimates represent what a trained SBM model can determine as the closest allowable normal physiological data that corresponds to the monitored data.
• the estimates made of the physiological data are differenced with the actual, monitored physiological data to generate residuals, representing the differences between the expected values according to the trained model, and what has been measured by the wearable sensing device. These residuals form the basis for further analysis that provides early detection of subtle warning of health problems, which would likely be missed using conventional medical methods of comparing vital signs to demographically acceptable ranges (e.g., population-based standards for blood pressure).
• Residuals for normal physiology are different from residuals for physiology that is beginning to deviate from normal, and can be statistically distinguished.
• the further computerized analysis of the residuals comprises one or more of the steps of: determining a likelihood that the residuals derived for any given multivariate input observation of monitored data are representative of a pattern of residuals characteristic of normal physiology, based on a "mixture of Gaussians" density estimation; generating a multivariate health index based on that likelihood as a logarithm of the inverse of the likelihood; applying a threshold to the index thus generated to render a decision whether the inputted vital signs are characteristic of normal physiological behavior; and combining a series of such decisions to provide an early indication of deviation from normal of the physiological health of a patient.
• the multivariate health index advantageously summarizes the residual analysis from multiple variables into a single index for the management of prioritized lists of patients.
• the health monitoring solution can also be applied to multivariate physiological parameters obtained in a hospital from bedside monitors.
• An SBM model of typical human physiology can be used to make estimates and residuals for patients in the hospital, particularly those at risk for developing complications such as sepsis or pneumonia, and particularly patients who are sedated and/ or ventilated and not able to express discomfort or feelings of incipient illness.
• Bedside data feeds amenable to the health monitoring solution include electrocardiographs, pulse oximeters, ventilator data, arterial and venous pressures measured by noninvasive means or by catheters, and the like. Such data can be streamed to a server for the hospital ward, or to off-site servers for monitoring multiple hospital facilities, and decision support can be rendered by application of SBM to these data streams and displayed to healthcare workers for prioritizing patient treatment.
• the analytics of the present invention can be performed on generic computing platforms specially configured by software.
• Data collected from sensors on the patient can be wirelessly transmitted to an ambulatory or portable device, e.g., via Bluetooth or other extremely local radio protocol.
• the portable device can be a cell phone carried by the patient, a "personal digital assistant", PDA, or the like, or a portable computing device moved with a patient in the hospital bed.
• This device may receive raw sensor signals and perform the aforementioned preprocessing to extract vital sign "features" (physiological data) from the sensor signals, for example a heart rate from an EKG/ECG signal; or may receive already-preprocessed features extracted by sensor microprocessing facilities from raw sensor signals.
• the resulting physiological "feature" data can be analyzed with SBM either on the device (the cell phone or PDA) or on a computer/server to which such physiological data is transferred.
• the computer can be a home computer collocated with the patient, or can be a remote server at an analytics data center.
• the transfer of data from the device can be by means of cabled offload or by wireless retransmission.
• FIG. 1 is a block diagram showing a general arrangement according to one embodiment
• FIG. 2 shows an example of sensor placement on a human
• FIG. 3 shows an example chart of raw physiological waveforms or signals
• FIG. 4 shows a signal amplitude chart of photoplethysmography components used to determine a feature related to Sp02 (blood oxygen saturation), which may be understood to represent the light components picked up by a photosensor stacked additively;
• FIG. 5 is a multi-chart example plot showing in the top four plots raw physiologically-related signals, and in the bottom five plots the related feature data derived there from;
• FIG. 6 is a plot of an exemplary physiological feature time series showing perturbations of that time series used in accuracy and robustness calculations
• FIG. 7 A is one of a pair of related plots of a multivariate health index and has been derived merely for raw feature data showing an index for unperturbed data and for perturbed data;
• FIG. 7B is a multivariate health index plot derived for residual data generated from kernel-based models of feature data showing and index for unperturbed data and for perturbed data;
• FIG. 8 is a block diagram showing an alternative embodiment.
• a patient may have heart failure, chronic obstructive pulmonary disease, renal failure, diabetes, early stage dementia and other conditions, which can devolve from a stable, managed state into an emergency health risk with little apparent warning. It is desirable to detect such devolution early because medical intervention at the early stage can prevent the emergency, avoid costs, prevent disease progression, and improve outcomes.
• SBM is a multivariate approach that takes advantage of the interrelationships between vital signs signals (e.g., heart rate (HR), blood oxygen saturation (Sp02), Respiration Rate, Blood Pressure).
• vital signs signals e.g., heart rate (HR), blood oxygen saturation (Sp02), Respiration Rate, Blood Pressure.
• HR heart rate
• Sp02 blood oxygen saturation
• Respiration Rate Blood Pressure
• SBM achieves these advantages by embodying normal variation in a model ("learning"). This model is then used to generate multivariate estimates of the learned physiological parameters when presented with a multivariate measurement of those parameters.
• the residuals are analyzed using a multivariate density estimation technique.
• the multidimensional distribution of residual vectors vectors of dimension n where n is the number of physiological parameters for which estimates were differenced with actual measured values
• the density estimator is a Gaussian mixture model, and is used to determine the likelihood that any new input residual vector (i.e., from newly monitored data) is part of the same distribution.
• This likelihood obtained from the multidimensional density estimator effectively consolidates the behaviors of the individual residuals for each of the physiological parameters, into one overall index that can be used to summarize patient priority.
• This likelihood can be used as a multivariate health index (MHI), and can be subsequently tested with a number of persistence rules to assess patient priority over a time series of observations of the multiple physiological parameters being monitored.
• MHI multivariate health index
• step 105 multiple biosignals are acquired from sensors on or in the patient.
• appropriate biosignals include electrocardiographs (ECG), thoracic bioimpedance (bio- Z), photoplethysmographs (PPG), temperature differentials, systolic or diastolic blood pressures, accelerometer-measured motion, piezoelectric signals of respiratory activity, and instant airflow measurements from respiration, to name a few.
• these biosignals are used to derive physiological feature data.
• a variety of physiological features can be derived from such biosignals, with a commonly understood example being heart rate determined from landmarks of the ECG signal.
• thoracic bioimpedance can yield respiratory rate and depth;
• PPG can yield pulse transit time (when cross referenced to the ECG) and the blood oxygen saturation, and so on.
• a variety of physiological features are known in the art, and the application of SBM in subsequent steps readily contemplates the use of new features as well, because the method is agnostic to the signals used (as long as the model is trained on the same kind of data) so long as they interrelate through the feedback loops and control mechanisms of human physiology.
• the derived features can be supplemented with other physiologically-relevant data, that is, data that impacts the physiological behavior or response of the monitored human.
• physiologically-relevant data that is, data that impacts the physiological behavior or response of the monitored human.
• Fi02 the fraction of oxygen in inspired air, which can be increased over room air with the use of supplementing oxygen.
• a kernel-based model such as SBM that has been trained on normal variation of these same physiological features generates estimates of an input observation of the features. Typically, an estimate is made for all elements in an input vector comprised of the collection of physiological parameters sampled contemporaneously.
• the residuals are generated between those features measured and corresponding estimates of those features, in the instant monitored observation.
• threshold tests can be applied in a univariate manner or in a multivariate pattern-matching manner to the residuals in step 130.
• the residuals are processed in step 135 by a mixture model developed from "normal" residuals, and a multivariate health index is determined for the input observation in step 140.
• This MHI is an index of the likelihood that the residuals from the input observation belong to the multivariate distribution of the mixture model.
• the MHI can also be tested with a threshold to determine if the likelihood is insufficient such that the input observation evidences deviations not characteristic of normal physiology.
• persistence rules can be applied to a time series of MHI determinations to further test observation-over-observation in time the persistence of threshold exceedances, providing greater confidence that a deviation is occurring in the patient's health, and is not merely a transient phenomenon in the data.
• the alerts from the MHI and its test, along with any previous tests on individual residuals or residual patterns, is managed for prioritization of patient care via a user interface. Alert management can facilitate user-initiated annotations into a medical record system relating to the alerts of "dismissal", “elevation” or “monitor” and other actions.
• the biosignals of step 105 can be acquired from typical hospital vital signs equipment such as bedside monitors and ventilators, from mobile vital signs monitors, implanted devices such as implantable cardioverter defibrillators and pacemakers with instrumentation, and from wearable ambulatory monitors. Whatever data source device is used, it must collect biosignals capable of providing multiple related physiological variables or features contemporaneously and at least periodically, if not continuously.
• a patient uses a non-invasive ambulatory sensing device or has an implantable device to acquire biosignals on at least a semi-continuously basis throughout the patient's normal daily activities.
• Data acquired by a sensing device can be offloaded from device memory on a periodic basis and thereafter processed on a computer; or can be continuously transmitted by cellular network or WiPi, to be processed either continuously or in batch-mode by a receiving computer or server.
• the physiological features can even be analyzed using the residual-based method on a smartphone or PDA, carried by the patient, since the computing requirements of the analytical process are well within the capabilities of modern mobile devices. Then, resulting alerts or health status conditions can be reported locally on the mobile device, and can also be uploaded to a central server to be shared with medical practitioners.
• One non-invasive wearable sensing device that can be used with the present invention is designed to acquire data from 4 types of signals: ECG, red and infrared (IR) photoplethysmograph (PPG), bioimpedance, and a 3-axis accelerometer. These sensors provide a rich waveform set from which physiologic features can be extracted. The extracted features (as opposed to the raw waveform data) are what ultimately drive the SBM-based human health monitoring approach.
• the device can be designed to record relevant biosignals for local storage, e.g., on an onboard microSD card; or for transmission via a built-in Bluetooth radio to a cell phone or PDA carried by the patient.
• the device can be designed to have a USB Mini-B connector that can be used to supply power to the device when recharging its battery, and that provides a mechanism for high-speed communication with a PC for periodically off-loading data, if raw real-time sensor data are stored on a micro-SD card of the device.
• the device may use a microprocessor selected from the well known Texas Instruments MSP430 line, ideal given its low power consumption characteristics, built-in ADC, DAC, timers, and multiple serial peripheral interfaces (SPI/UART/I2C).
• the Bluetooth interface can be provided via a BlueCore 3 Plug-n-Go IC, a 96-pin BGA module from CSR, Inc., with minimal external component requirements, and a 2.4 GHz chip antenna.
• a number of sensing interfaces can be used to provide data for the present invention.
• the electrocardiogram (ECG) can be implemented by using a two-stage analog high pass filter (HPF), followed by a radio-frequency interference (RFI) filter and a micro-power instrumentation amp. It is crucial in an ambulatory mode to employ an RFI filter in front of this high gain differential amplifier. Without it, a phenomenon called RF rectification can occur in the differential amplifier IC. Once an RF signal becomes rectified inside the IC, it results in a DC offset error at the output and no amount of low pass filtering can remove the error. As the RFI changes over time the DC offset changes as well resulting in an ECG signal that is highly susceptible to artifacts.
• Two pickup electrodes can be used to acquire the signal, for example on either side of the chest.
• the ECG is typically sampled at 12 bits and 256 Hz by the microprocessor.
• a bioimpedance measurement can be made by using a dedicated 12-bit impedance converter network analyzer IC (Analog Devices AD5933) in conjunction with a voltage to current stage and a programmable gain instrumentation amplifier.
• An electrode placed under the left armpit can be used to inject 425 ⁇ of current at 50 kHz to a ground electrode found on the opposite side of the torso.
• the same electrodes used to pickup the ECG signal can be used to pick up the 50 KHz signal through a 5 KHz HPF and an RFI filter.
• the AD5933 IC is capable of measuring the complex impedance of the signal.
• the PPG signal can be acquired by controlling a pair of LEDs (Red and Infrared) via a current limiting H-Bridge for light generation.
• the unabsorbed light is measured using a reverse-biased PID photodetector connected to a transimpedance amplifier for initial gain.
• the measured signal is then fed to a second stage differential amplifier along with a DC-offset value generated in firmware from the output of the microprocessor's DAC.
• the DC-offset value is meant to keep the signal within the rails of the differential amplifier so that the signal gain can be maximized.
• the output of the second stage amplifier is preferably then oversampled by a factor of 8 at 16384Hz (for a final sampling rate of 256 Hz) after a waiting period of 488 uS after the LEDs have changed states.
• the oversampling is applied to increase the signal-to-noise ratios of the PPG signals, which are highly susceptible to noise.
• Accelerometer data can be generated by a LIS302DL MEMS digital accelerometer at 400 Hz (8 bits per axis). The digital readings are preferably read by the microprocessor at a rate of 100 Hz.
• the acquired data can be placed into two buffers: one that is flushed out to the file system (micro-SD), and one that is fed to the Bluetooth IC for transmission.
• Each value is preceded with a single byte ID for identification, and periodic "sync" blocks are inserted into the Bluetooth stream to aid in data alignment.
• Each packet of data consists of the ID byte, followed by two bytes containing the sample value.
• Periodic 32-bit timestamps are also transmitted by utilizing two packets to represent the high and low words of a 32-bit seconds counter.
• a subject is outfitted with four electrodes and one pulse oximetry sensor.
• Two types of electrodes can be used, carbon-rubber non-adhesive electrodes and carbon-rubber adhesive electrodes, although other commercially available electrodes are readily contemplated for use in the embodiment.
• the electrodes are placed on the body as shown in FIG. 2: (A) corresponds to the Bioimpedance current source electrode, (C) is the +ECG electrode, (F) is the -ECG electrode, and (H) is the analog ground electrode (AGND).
• the ECG leads are also used to simultaneously pick up the bioimpedance response signal.
• the device can be worn by either being placed in a stretchable chest strap with the non-adhesive electrodes attached to the inside of the strap via Velcro, or it is placed in a pouch worn around the neck with leads running to the adhesive electrodes.
• the PPG signal is acquired via a disposable Nellcor reflective pulse oximetry sensor affixed to the forehead and connected to the device.
• a typical example of the signals captured by the wearable sensing device described above from a human subject is shown in FIG. 3.
• the signals are: (A) ECG, (B) x-axis accelerometer, (C) infrared photoplethysmograph (PPG), (D) real component of bioimpedance, and (E) imaginary component of bioimpedance. Not shown are the y and z axis accelerometer signals, and the red PPG signal which are all captured as well.
• physiological feature generation the raw data collected from the wearable device is not directly analyzed with SBM. Instead a set of physiological features are derived from the raw waveform data. These derived features are what provide the insight into the status of human cardiopulmonary control system and in turn the overall health of an individual. According to one example, several features from two categories can be used, cardiac derived and respiratory derived.
• the cardiac derived features are heart rate (HR), pulse transit time (PTT) and the Red absorption to IR absorption PPG ratio (or Q).
• the HR feature can be obtained directly by measuring the interval between consecutive QRS peaks in the ECG signal. The peaks are detected using a multi-step procedure. First a digital HPF is applied to the ECG signal.
• the filtered signal is split into 10 second data windows that are de- trended to remove a straight line fit to the data.
• the 98th percentile is calculated and the locations of all samples above the 98th percentile are found. All samples found reside on a set of local peaks within the 10 second window.
• the last step is to find the sample location of the maximum value for each of the local peaks within the window. These locations are the individual QRS peaks in the ECG waveform.
• the HR rate is simply the reciprocal of the time interval between each heart beat.
• FIT is the delay time between the QRS peak and PPG pulse peak. This feature is known to be inversely proportional to blood pressure. To calculate it, the robustness of the ECG QRS peak detection algorithm is exploited with first principles. Since it is known that a transit time of more than 250 ms is unlikely in a human, 250 ms windows starting from the QRS peak location for each heart beat can be used to search for the corresponding PPG peak. The maximum value within the window is the PPG peak. This is done for both the red and IR PPG signals.
• the PPG signals tend to be naturally noisy, before the peaks are located, the PPG signals are first digitally filtered using a median filter (to remove spiking) followed by a band-pass filter with lower and upper cutoff frequencies of 0.5Hz and 5 Hz respectively.
• the Q feature is the ratio of the blood absorption of red light to infrared light.
• Q is inversely known to be proportional to Sp02 (blood oxygen saturation). Calculating Q is more complicated due to the analog and digital signal processing that takes place before the raw PPG data are acquired.
• Q is calculated as follows. The basic equation for Q is given by
• REDAC is the amount of red (infrared) light absorbed by the blood
• REDDC is the amount of red (infrared) light absorbed by the surrounding tissue.
• the PPG implementation comprises an LED driving stage, a PID photodiode with a transimpedance amplifier, and a second gain stage which subtracts out a DC offset (RED OUTPUTOFFSET in the FIG. 4) and adds additional gain.
• Some level of background light is detected by the sensor, and needs to be subtracted from the measured signal as well (OFF SIGNAL + OFF OUPUTOFFSET).
• the RED DC TRACK parameter is the lower envelop of the actual acquired signal. Then Q can be given by the following equations (shown for red only).
• RED'AC is the peak-to-peak value of the actual acquired PPG signal
• a and ⁇ are scaling factors that are function of the analog to digital converters.
• respiration rate RR
• TV tidal volume
• the bioimpedance signal is first bandpass filtered with a narrow band digital filter with lower and upper cutoff frequencies of 0.133 Hz and 1Hz (corresponding to a RR range of 8 to 60 breaths per minute).
• a sliding window Discrete Fourier Transform (DFT) is applied to the filtered data with overlap to produce feature values every 20 seconds.
• the RR rate feature corresponds to the frequency at which the maximum value of the magnitude of the DFT occurs in each window.
• each window of data is multiplied with a window function that suppresses the end points to zero before the DFT is calculated.
• TV is defined to be the value of the magnitude of the DFT at the RR frequency, and quantitatively relates to true tidal volume but is not a directly calibrated measure of tidal volume.
• two last steps are taken to finalize the feature generation process.
• a noise filtering step that removes spikes and smoothes the feature data at the same time
• a moving window trimmed mean filter is applied with 50% window overlap.
• the default window size is 40 seconds and with an overlap of 50% the resulting filtered features occur at a rate of 1 sample every 20 seconds.
• the second step is to align all the feature data in time so that they can be analyzed with SBM. This is achieved by interpolating all of the filtered features at the same time points using a shape-preserving piecewise cubic interpolator. An example of the filtered features is shown in FIG.
• Data region 505 occurred while the subject held his breath as is evident by tidal volume (F) going to zero. During the same period the red to IR PPG ratio (I) starts to increase indicating that 02 saturation is lowering. Region 510 occurred while the subject was walking briskly around. After about 45 seconds into the walk his respiration rate, tidal volume and heart rate increase ((E), (F) and (G) respectfully).
• region 515 represents the subject running up and down a staircase three times with short rests in between. As expected, similar behavior to that of region 510 is seen.
• a strategy for detecting artifacts in the raw sensor data is based on a number of components.
• This approach works well for detecting transients but does not detect sensor problems.
• the second component combines heuristic rules with first principles rules to detect sensor and /or feature generation errors. The set of rules is summarized below:
• Calculating RR is based on extracting the maximum spectral component of the bioimpedance signal within a narrow band and if TV is below Ttv the person is not breathing, or is breathing so shallowly that the maximum component is meaningless; it's just the maximum noise component in the frequency band during this state.
• kernel-based multivariate estimator is a multivariate estimator that operates with a library of exemplary observations (the learned data) on an input observation using a kernel function for comparisons.
• the kernel function generally yields a scalar value (a "similarity") on a comparison of the input observation to an exemplary observation from the library.
• the scalar similarity can then be used in generating an estimate as a weighted sum of at least some of the exemplars. For example, using Nadaraya-Watson kernel regression, the kernel function is used to generate estimates according to:
• X n ew is the input multivariate observation of physiological features
• Xi are the exemplary multivariate observations of physiological features
• Xeet are the estimated multivariate observations
• K is the kernel function.
• exemplars comprise a portion Xi comprising some of the physiological features, and a portion Y i comprising the remaining features
• X new has just the features in X i
• Y est is the inferential estimate of those Y i features.
• all features are included in X new , Xi and in the X est together - all estimates are also in the input.
• the kernel function by one approach, provides a similarity scalar result for the comparison of two identically-dimensioned observations, which:
• kernel functions may be selected from the following forms:
• X a and X b are input observations (vectors).
• the vector difference, or "norm”, of the two vectors is used; generally this is the 2-norm, but could also be the 1-norm or p- norm.
• the parameter h is generally a constant that is often called the "bandwidth" of the kernel, and affects the size of the "field" over which each exemplar returns a significant result.
• the power ⁇ may also be used, but can be set equal to one. It is possible to employ a different h and ⁇ for each exemplar Xi.
• the measured data should first be normalized to a range of 0 to 1 (or other selected range), e.g., by adding to or subtracting from all sensor values the value of the minimum reading of that sensor data set, and then dividing all results by the range for that sensor; or normalized by converting the data to zero-centered mean data with a standard deviation set to one (or some other constant).
• a kernel function according to the invention can also be defined in terms of the elements of the observations, that is, a similarity is determined in each dimension of the vectors, and those individual elemental similarities are combined in some fashion to provide an overall vector similarity. Typically, this may be as simple as averaging the elemental similarities for the kernel comparison of any two vectors x and y:
• elemental kernel functions that may be used according to the invention include, without limitation:
• the bandwidth h may be selected in the case of elemental kernels such as those shown above, to be some kind of measure of the expected range of the m" 1 parameter of the observation vectors. This could be determined, for example, by finding the difference between the maximum value and minimum value of a parameter across all exemplars. Alternatively, it can be set using domain knowledge irrespective of the data present in the exemplars or reference vectors, e.g., by setting the expected range of a heart rate parameter to be 40 to 180 beats per second on the basis of reasonable physiological expectation, and thus h equals "140" for the m* parameter in the model which is the heart rate. [0052] According to one approach, Similarity-Based Modeling is used as the kernel- based multivariate estimator.
• SBM models can be used for human data analysis tasks: 1) a fixed SBM model, 2) a localized SBM model that localizes using a bounding constraint, and 3) a localized SBM model that localizes using a nearest neighbor approach.
• the fixed SBM modeling approach generates estimates using the equation below.
• D is a static m-by-n matrix of data consisting of n training data vectors with m physiological features, pre-selected from normal data during a training phase.
• the kernel function K is present as a kernel operator ® whereby each column vector from the first operand (which can be a matrix, such as D is) is compared using one of the kernel functions described above, to each row vector of the second operand (which can also be a matrix).
• the monitored input observation is here shown as x in (t)
• the autoassodative estimate is shown as (/) .
• LSBM localized SBM
• the D matrix is redefined at each step in time using a localizing function F( ) based on the current input vector x in (t) and a normal data reference matrix H.
• matrix H contains a large set of exemplars of normal data observations, and function F selects a smaller set D using each input observation.
• F can utilize a "nearest neighbor" approach to identify a set of exemplars to constitute D for the current observation as those exemplars that fall within a neighborhood of the input observation in m- dimensional space, where m is the number of features.
• function F can compare the input observation to the exemplars for similarity using a kernel-based comparison, and select a preselected fraction of the most similar exemplars to constitute D.
• Other methods of localization are contemplated by the invention, including selection on the basis of fewer than all of the physiological features, and also selection on the basis of a distinct parameter not among the features, but associated with each exemplar, such as an ambient condition measure.
• Models used for estimation in the present invention are preferably empirical models determined from data, in contrast to first-principles models that relate parameters by deterministic equations. Therefore, instead of deriving a model, the model must be trained with empirical data.
• Training a model of physiology comprises gathering exemplary observations of the physiological parameters or features to be modeled and building a reference library of exemplars. These features can be range- normalized, or can be used in their native units of measurement in combination with an elementary kernel function, such as those shown in equations 10-12, that uses a bandwidth that is proportional to the expected range in those native units of measure.
• observations are obtained of the features in question from the patient who will be monitored, during conditions in which the patient is deemed to be medically normal or medically stable.
• the patient need not be in pristine health, as the method of the present invention looks for relative change.
• the normal data preferably includes representation from all manner of activity that is to be modeled, and need not be limited to highly immobile, sedated or "steady state” conditions, unless those are the only conditions that will be modeled. Exemplars are typically just observations selected for inclusion in the reference library from the larger set of available normal observations; exemplars can also be determined as computed "centers" of clustered normal data in the alternative.
• the model can be used to generate estimates responsive to monitored input observations. With each input observation, an estimate of at least some of the physiological features is generated according to one of the embodiments of equations 4, 5, 13 or 14 above. The estimated features are then differenced with the measured values of those features in the instant observation to create a residual for each such feature. Given that real-world signals have inherent measurement noise and inherent system noise, and given that empirical models will have some inherent inaccuracy, residuals will occur not only for deviating data from deteriorating physiology, but also for data from normal physiology.
• a number of well known methods for testing raw data can be applied to the residuals, including thresholds.
• a threshold can be applied to a residual such that small variations are tolerated, by larger values trigger an alert.
• Series of decisions on residuals for individual physiological parameters can be the basis for rules relating to the genuine existence of a persistent deviating health condition, for example by counting the number of threshold exceedances in a window of observations. Rule patterns can be applied across residuals for different physiological features, triggered only when the pattern of deviations in the residuals is identified.
• SBM is removing the normal variation in the actual data and leaving behind abnormal data in the form of residuals (normal as defined by the training data).
• the performance of a model can be measured using a nonparametric perturbation-based approach that is particularly well suited for comparing modeling techniques used for anomaly detection applications.
• the performance of a model is assessed using three metrics: 1) robustness, 2) spillover and 3) error.
• the robustness metric is a measurement of the likelihood that a model will follow (or over-fit) a perturbation introduced into the data.
• To measure robustness first estimates for all of the variables in a model are made based on a test data set containing normal data ( 0 in FIG. 6). Next, a perturbation ⁇ is added to each variable one at a time in the model as shown ( ⁇ ⁇ in FIG. 6). Finally, estimates are generated for each of the perturbed variables (Jc n FIG. 6).
• the robustness metric for each variable in a model is then given by the following equation:
• the spillover metric measures the relative amount that variables in a model deviate from normality when another variable is perturbed. In contrast to robustness, spillover measures the robustness on all other variables when one variable is perturbed. The spillover measurement for each variable is calculated using a similar calculation, which is given by
• the error metric is simply the root mean squared error of the difference between the actual value and its estimate divided by the standard deviation of the actual value, or equivalently the residual RMS divided by the actual value standard deviation:
• the equations listed above define the metrics for each variable in a model. In each case, a smaller value is better.
• the overall performance metrics for a model are calculated by averaging the results for each variable in each case.
• a multivariate density estimation approach can be applied to the residual data.
• the approximated densities in the normal behavior of the data are used to determine the likelihood (in the form of a multivariate health index (MHI)) that a new data point is part of the normal behavior distribution.
• MHI multivariate health index
• the density estimates are calculated using a non-parametric kernel estimator with a Gaussian kernel. The estimator is shown in the equation below.
• the resulting density function is essentially a mixture of N individual multivariate Gaussian functions each centered at Xi:
• N is the number of training vectors
• h is a bandwidth parameter
• d is the dimensionality of the vectors
• f (x) is a scalar likelihood.
• the X and Xi are not multivariate observations of physiological features, but are instead multivariate residual observations derived from the original observations by differencing with the estimates.
• the density "estimation” here is not the same as the estimation process described above for estimating physiological feature values based on measured values; the "estimate” here is empirically mapping out a probability distribution for residuals using the normal multivariate residual exemplars, as a Gaussian mixture model.
• the exemplars Xi can be selected from regions of normal data residuals generated by SBM using test data that is deemed "normal” or representative of desired or stable physiological behavior. Before the density estimates are made, all residuals are scaled to have unit variance and zero mean, or at least are scaled to have unit variance. The means and standard deviations used for the scaling procedure are calculated from known normal data residuals.
• the multivariate health index (MHI) in one form is a function of / (x) and is given by:
• equation 18 the likelihood determined from equation 18 need not be converted as in equation 19 in order to be useful, and equation 19 is used primarily to invert the signal trend (so that higher equates to rising health risk). Tests may be applied directly to the result of equation 18.
• FIGs. 7A-7B A comparison of the efficacy of applying the multivariate density estimation approach to residuals is highlighted in FIGs. 7A-7B.
• Chart 705 shows a multivariate density estimation similar to that described above except applied to raw physiological feature data (the actual values of heart rate, respiration rate, etc.); while chart 710 (FIG. 7B) shows the multivariate density estimation as described above applied to residuals generated from a kernel-based model (SBM).
• SBM kernel-based model
• MHI results are shown for physiological data both unperturbed (normal) and with an artificially- induced perturbation (abnormal). The perturbation was introduced as a slow drift in a subset of ambulatory physiological features from the start of the data, with a maximum drift achieved at the end of the data.
• the MHI computed for "normal” unperturbed data is shown as a solid line
• the MHI computed for "abnormal” perturbed data is shown as a dotted line.
• a detection threshold (717, 720) was determined for each approach based on statistics for a test set of normal data, where the statistics were for raw data in the case of chart 705 and for residuals in the case of chart 710.
• a decision algorithm was further applied to the MHI to ascertain a persistent, reliable threshold exceedance alert, in this case x successive MHI threshold exceedances yields an alert decision. The decision can be latched until a series of y successive values for MHI are observed below the threshold, in which case the alert is removed.
• an alert can be latched when there have been x threshold exceedances in a window of m observations, and the alert removed when there have been y observations below the threshold in a window of b observations.
• the vertical line (730, 735) indicates the point at which a decision was made that the data are not from the normal behavior distribution and hence indicate an abnormal condition.
• detection occurs about one-third of the way from the start of the simulated disturbance for the residual-driven MHI, whereas detection using raw data in combination with a multivariate density estimation does not occur until much later in the data. This is due to the combination of a model of normalcy removing normal variation, with the multivariate density estimation of likelihood of normalcy applied to residuals.
• This residual-based MHI method has the novel advantages of providing substantially earlier detection of an incipient pattern of deviation in health, and providing a single index of patient deviation to summarize individual residuals for the multiple physiological features being monitored.
• the system described herein can be deployed to provide predictive monitoring of patient health in an ambulatory, at-home environment, particularly for patients with chronic diseases that may deteriorate unpredictably.
• Multiple physiological features are derived from one or more biosignals and parameters captured from a wearable or implanted device (or both), and transmitted to an analytics data center, where one or more servers are disposed to process the physiological features using empirical, kernel-based models.
• the models are preferably personalized to the data from the patient captured during periods when the patient is considered to be in normal or acceptably stable health, to provide a model of normal physiology for the patient.
• Monitored data is estimated using the personalized model, and the monitored values are differenced with the estimated values of the physiological parameters to yield residuals.
• the residuals are then processed through one or more methods of analysis to yield alerts regarding the patient's health status.
• the residuals can individually be tested with rules, such as thresholds. These thresholds can further be tested for persistence. Patterns of residual tests can be recognized to yield even more specific health status information.
• the multivariate observation of residuals can be examined for likelihood of belonging to a "normal" residual distribution using an empirical multivariate probability density estimation, and this likelihood may then be converted to a multivariate health index, typically as an inverse log value of the likelihood.
• the MHI provides an instant ranking of patient health status, and the MHI can be tested using a threshold, as well as persistence rules, to yield alerts regarding patient health status.
• All such analytics can be presented via a web- based or client-server-based user interface to medical practitioners, and in this way a large population of patients can be monitored together by medical staff with improved efficiency.
• All such monitored patients of a health care institution or practice group can be managed for early warning of deteriorating health at home, and the patients can be prioritized for specific follow-up based on health status.
• Patients with early indications of health deterioration can be contacted to verify compliance with medications, inquire about how the patient feels, and investigate recent patient behavior that may have exacerbated a chronic illness.
• Medical staff may advantageously avert a more costly health emergency for the patient with efficient interventions including instructing the patient to make adjustments to medications, comply with medications, or come in for an examination and preventative intervention.
• SBM can also be deployed with cross subject modeling, instead of an entirely personalized model.
• a model then comprises data from other human subjects. Due to the person to person variation in feature data it is necessary to scale each subject's data.
• a generic cross population model can be used as a temporary means for monitoring a human when no historical data are available for the individual as long as the individual's feature data are properly scaled.
• the scaling can be accomplished based on statistics calculated during a standardized set of activities when the monitoring device is first put on.
• the data acquired during the standard activities (which can comprise lying down, sitting, standing, walking and climbing stairs, for example) is typically scaled to a zero-mean, one-standard deviation range.
• the monitoring is not as sensitive as it would be for a personalized model but it at least provides a minimal level of health monitoring while waiting to acquire a suitable set of data to generate a personalized model.
• FIG. 8 another approach obtains residuals from reference data representative of a known illness, malady or health deterioration, so that a multivariate probability density estimator can be determined for that health deterioration, in contrast to determining it for normal or stable health.
• one or more probability density estimators 810 can be created in this way (including one for normal data), and applied to multivariate residual observations 820 from monitored data 830.
• Likelihoods that the monitored residual observation belongs to each of the distributions can be compared in parallel in a decisioning step 840, and not only can deviation from normal be detected, but the nature of the health deterioration can be categorized.
• Likelihoods can simply be displayed to medical staff, or the likeliest scenario or the set of scenarios with a sufficiently high likelihood can be indicated as the probable state(s) of the patient in 840.
• the likelihoods or MHI values for each of a plurality of maladies are normalized using test statistics generated from known examples of each such malady processed through model estimation and residual generation, so that they can be expressed in terms of the typical variance expected for residual vectors fitting each such category. Then the normalized values are compared to determine which category is in fact most likely represented by the current monitored data. Series of MHI or likelihood values for each malady category can also be processed heuristically to rank categories, for example with moving window averages or medians.
• patients in a hospital are monitored with multivariate physiological parameters derived from sensors using conventional bedside monitors, ventilators, and/or wearable or implanted devices.
• Data is streamed via Ethernet network or WiFi to a central station / nursing station or to a hospital centralized data center, coupled to interfaces for medical staff real-time monitoring.
• Data is also streamed via Ethernet network or WiFi to analytics server(s) for processing using empirical, kernel-based models as described herein. Estimates are made of the physiological features, and residuals are generated; models may be generic instead of personalized, since no personal data may be available for a patient from a period when that patient was in acceptable physiological health.
• a model can comprise data from other humans collected in similar hospital conditions when the humans were in acceptable health.
• Such a model can further be tailored to the monitored patient on the basis of major contributors to normal physiological variation, such as body mass, gender, age, and medical condition (e.g., similar cardiac ejection fraction or similar respiratory performance). Residuals are processed as described above to generate MHI and /or rules-based decisions.
• Patient health status for all monitored patients in the ward or hospital or ICU can be monitored by onsite medical staff or off -site medical staff to provide early warning of developing health issues, such as infection, pneumonia, and sepsis.
• the health alerts of patients can be managed in a proactive manner, rather than being a crisis that must be immediately responded to.
• the user interface provides for several levels of alert management: Alerts can be dismissed (investigation by medical staff shows the alert to be anomalous); alerts can be confirmed and elevated (investigation by medical staff shows a definite health issue is present that needs intervention); and alerts can be marked for further follow-up and observation (investigation shows close monitoring is warranted but immediate intervention is not required or advised).
• a system for advanced warning of health problems, using a wearable sensing device for capturing rich physiological data streams from a human outside the hospital, in the daily routine of their home life, providing high visibility into a patient's physiological status outside the reach of the physician's office or the hospital ward.
• Automated processing of this data using algorithms that remove the normal variation present in ambulatory data, to provide robust and early detection of anomalies indicative of incipient health issues is novel and inventive.
• the potential for this combination of device plus algorithm to revolutionize patient care is enormous, especially for the chronically ill patient population. This platform is exactly the kind of tool needed by physicians to improve patient outcomes, avoid unnecessary costs, and greatly extend the leverage of the medical workforce.

Landscapes

• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Engineering & Computer Science (AREA)
• Medical Informatics (AREA)
• Public Health (AREA)
• Pathology (AREA)
• Biomedical Technology (AREA)
• General Health & Medical Sciences (AREA)
• Physics & Mathematics (AREA)
• Biophysics (AREA)
• Veterinary Medicine (AREA)
• Animal Behavior & Ethology (AREA)
• Surgery (AREA)
• Heart & Thoracic Surgery (AREA)
• Molecular Biology (AREA)
• Physiology (AREA)
• Artificial Intelligence (AREA)
• Psychiatry (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Signal Processing (AREA)
• Cardiology (AREA)
• Data Mining & Analysis (AREA)
• Databases & Information Systems (AREA)
• Epidemiology (AREA)
• Primary Health Care (AREA)
• Pulmonology (AREA)
• Evolutionary Computation (AREA)
• Fuzzy Systems (AREA)
• Mathematical Physics (AREA)
• Multimedia (AREA)
• Immunology (AREA)
• Vascular Medicine (AREA)
• Measuring And Recording Apparatus For Diagnosis (AREA)

Priority Applications (5)

Applications Claiming Priority (2)

Publications (1)

Family

ID=44259034

Family Applications (1)

Country Status (7)

Cited By (8)

Families Citing this family (125)

Citations (4)

Family Cites Families (110)

• 2011
  • 2011-01-04 EP EP11733194.2A patent/EP2523625B1/en active Active
  • 2011-01-04 WO PCT/US2011/020094 patent/WO2011087927A1/en not_active Ceased
  • 2011-01-04 CA CA2787170A patent/CA2787170C/en active Active
  • 2011-01-04 US US12/984,400 patent/US8620591B2/en active Active
  • 2011-01-04 CN CN201180013786.5A patent/CN102917661B/zh active Active
  • 2011-01-04 JP JP2012548966A patent/JP5859979B2/ja active Active
  • 2011-01-04 AU AU2011205557A patent/AU2011205557A1/en not_active Abandoned
  • 2011-01-04 CA CA2998940A patent/CA2998940A1/en not_active Abandoned
• 2013
  • 2013-11-07 US US14/074,586 patent/US20140107433A1/en not_active Abandoned
• 2016
  • 2016-03-22 AU AU2016201807A patent/AU2016201807A1/en not_active Abandoned
• 2017
  • 2017-10-04 AU AU2017239519A patent/AU2017239519A1/en not_active Abandoned
• 2018
  • 2018-07-17 US US16/037,045 patent/US20180325460A1/en active Pending
• 2019
  • 2019-03-22 AU AU2019202009A patent/AU2019202009A1/en not_active Abandoned
• 2020
  • 2020-08-05 AU AU2020213311A patent/AU2020213311A1/en not_active Ceased
• 2021
  • 2021-06-08 AU AU2021203784A patent/AU2021203784B2/en active Active

Patent Citations (4)

Non-Patent Citations (3)

Also Published As

Similar Documents

Legal Events