Classifications

• • 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
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
• • 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
  • A61B5/72—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
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—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
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes

Definitions

• the plurality of features or machine-learned-based analyses are configured to quantify the beat-to-beat variations in cardiac signals.
• the plurality of features or machine-learned-based analyses are configured to quantify properties of cardiac, PPG, and/or SCG signals over loop regions (e.g., atrial depolarization, ventricular depolarization, and ventricular repolarization) in 3D phase spaces, projections thereof, and loop vectors.
• loop regions e.g., atrial depolarization, ventricular depolarization, and ventricular repolarization
• the apparatus further includes a plurality of surface electrodes configured to be placed on surfaces of a chest region of a subject to provide a plurality of cardiac signals of the subject’s heart, wherein the plurality of cardiac signals are provided to the analysis system to evaluate for the plurality of features or machine-learned- based analyses to generate the estimated value for the presence, non-presence, and/or severity of hypertrophic cardiomyopathy.
• FIG. 4A shows a diagram of an example SCG/PCG measurement device in accordance with an illustrative embodiment.
• Fig. 4B shows a diagram of another example SCG/PCG measurement device in accordance with an illustrative embodiment.
• Fig. 5A shows a schematic diagram of an example clinical evaluation system configured to use machine-learned-based analyses (among other analyses) to generate one or more metrics associated with the physiological state of a patient, including, e.g., presence, nonpresence, and/or severity of HCM, another condition, or indications and/or severity of either, in accordance with an illustrative embodiment.
• biophysical signals into types or categories that can include, for example, electrical (e.g., certain cardiac and neurological system-related signals that can be observed, identified, and/or quantified by techniques such as the measurement of voltage/potential (e.g., biopotential), impedance, resistivity, conductivity, current, etc.
• electrical e.g., certain cardiac and neurological system-related signals that can be observed, identified, and/or quantified by techniques such as the measurement of voltage/potential (e.g., biopotential), impedance, resistivity, conductivity, current, etc.
• Enlargement effects (202’).
• the septal wall (204) shown as ventricular septum 204
• ventricular septum 204 is enlarged that it infringes on the left ventricular outflow tract (see 206); it can create a physiological scenario similar to an aortic valve blockage that blocks the blood flow out of the left ventricle (208) into the aorta (201).
• This blockage or impediment of flow (203’) has been observed as turbulent flow through the narrowed left ventricular outflow tract (LVOT), causing “murmurs” (obstruction murmurs) or sounds and vibrations similar to all of the physiological effects of aortic valve stenosis (narrowing of the exit of the left ventricle of the heart).
• left atrial pressure represents the pulmonary venous pressure
• LVP left ventricular pressure
• diastolic pressure pressure the blood is exerting against the artery walls when the heart is relaxed between beats.
• the heart can increase the pulmonary venous pressure (211’), which can cause congestion (fluid buildup in the lungs) and shortness of breath, among other effects.
• the effect of the heart muscle being able to squeeze adequately but not relax adequately to fill for the next heartbeat can also lead to the body compensating by increasing arterial and venous blood pressure; this can also lead to shortness of breath and other conditions.
• Analysis system 104 includes the analytical engine comprising the analytics feature analysis module 110 configured to compute features or parameters to generate, via a classifier (e.g., machine-learned classifier), one or more estimated metrics associated with the presence, non-presence, and/or severity of hypertrophic cardiomyopathy.
• a classifier e.g., machine-learned classifier
• the analysis and associated modules may evaluate the provided features in relation to SCG or phonocardiographic signals, in addition to the PPG and cardiac signals, as well as extend the various analyses (frequency, dynamics, cycle variability, etc., as noted above), to assess for changes in the values of the features between inspiration and expiration portion of the signals.
• device 118 and associated sensors 126 and 128 are non-invasively placed on the skin of a subject at illustratively shown regions.
• the cross-section of the heart is shown in Fig. 1 merely to provide illustrative placement of the sensors in relation to certain heart structures in certain embodiments.
• Fig. 4B shows the example SCG/PCG measurement device 118 (shown as 118a) of Fig. 4A configured as a wireless measurement module comprising a wireless transceiver module 402.
• the wireless transceiver module 402 includes front-end conversion circuitries 403, a microcontroller 404, and a wireless transceiver circuit 406.
• the wireless transceiver circuit 406 can interface over a wireless connection to an interface device.
• the cardiac signals 308 and photoplethysmographic signals 310 may be acquired using circuitries and computing hardware, software, firmware, middleware, etc., in a biophysical signal capture system described in U.S. Patent No. 10,542,898, entitled “Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition,” or U.S. Patent Publication No. 2018/0249960, entitled “Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition,” each of which is hereby incorporated by reference herein in its entirety.
• different versions of the clinical evaluation system 500 may implement the assessment system 103 (Fig. 1) by having included containing different feature computation modules that can be configured for a given disease state(s), medical condition(s), or indicating condition(s) of interest.
• the clinical evaluation system 500 may include more than one assessment system 103 and may be selectively utilized to generate different scores specific to a classifier 112 of that engine 103.
• the modules of Figs. 1 and 5 in a more general sense, may be viewed as one configuration of a modular system in which different and/or multiple engines 103, with different and/or multiple corresponding classifiers 112, may be used depending on the configuration of the module desired. As such, any number of embodiments of the modules of Fig. 1 may exist.
• Clinical evaluation system 500 includes one or more feature libraries 526 that store the machine-learned-based analyses, e.g., as features.
• the feature libraries 526 may be a part of the add-on modules 502 (as shown in Fig. 5A) or the base system 504 (not shown) and are accessed, in some embodiments, by the AE add-on module 514.
• Signal quality assessment/rej ection (530).
• the base analytical engine or analyzer 506 assesses (530), via SQA module 516, the quality of the acquired biophysical-signal data set while the analysis pipeline is executing.
• the results of the assessment e.g., pass/fail
• the base analytical engine or analyzer 506 performs two sets of assessments for signal quality, one for the electrical signals and one for the hemodynamic signals.
• the electrical signal assessment (530) confirms that the electrical signals are of sufficient length, that there is a lack of high-frequency noise (e.g., above 170 Hz), and that there is no power line noise from the environment.
• the hemodynamic signal assessment (530) confirms that the percentage of outliers in the hemodynamic data set is below a pre-defined threshold and that the percentage and maximum duration that the signals of the hemodynamic data set are railed or saturated is below a pre-defined threshold.
• Outlier Assessment and Rejection Detection (538). Following the AE add-on module 514 computing the feature value outputs (in process 532) and prior to their application to the classifier models (in process 534), the AE add-on module 514 is configured in some embodiments to perform outlier analysis (shown in process 538) of the feature value outputs.
• Outlier analysis evaluation process 538 executes a machine-learned outlier detection module (ODM), in some embodiments, to identify and exclude anomalous acquired biophysical signals by identifying and excluding anomalous feature output values in reference to the feature values generated from the validation and training data.
• ODM machine-learned outlier detection module
• the outlier detection module assesses for outliers that present themselves within sparse clusters at isolated regions that are out of distribution from the rest of the observations.
• Figs. 6D and 6E it can be observed that the ensemble average output, as described in relation to Fig. 6C, can readily allow for the identification of pronounced murmurs (II-IV) in clean, well-behaved signals.
• Fig. 6D shows the ensemble average outputs for an acoustic signal with no murmur.
• Figs. 6E shows the ensemble average outputs for a set of acoustic signals with various stages of holosystolic (shown as “I/VI early-systolic” 608, “II/VI Holosystolic” 610, and “III/VI Holosystolic” 612).

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• 2023
  • 2023-01-23 WO PCT/IB2023/050550 patent/WO2023139554A1/en not_active Ceased
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