Method and system for signal quality assessment and rejection using heart cycle variability
The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.
1 . A method for rejecting an acquired measurement used for non-invasively assessing a disease state or abnormal condition of a subject, the method comprising:
obtaining, by instrument measurement system, a biophysical signal data set of the subject associated with a photoplethysmographic signal or a cardiac signal, wherein the biophysical signal data set is acquired from the subject with a cardiac measurement equipment of the instrument measurement system, wherein the biophysical signal data set comprises two or more channels of an acquired biopotential signal data set acquired by a first set of probes of the instrument measurement system associated with the cardiac signal or two or more channels of an acquired optical signal data set associated with the photoplethysmographic signal acquired by a second set of probes of the instrument measurement system;
determining, by the one or more processors and/or remotely by one or more cloud-based services or systems, one or more values associated with a heart cycle variability parameter using the obtained cardiac signal and/or the photoplethysmographic signal of the biophysical-signal data set; and
rejecting, by one or more cloud-based services or systems, any analyses of the biophysical signal data set by the one or more cloud-based services or systems, when the one or more values associated with the heart cycle variability parameter exceeds a predefined variability threshold, wherein the rejection generates a notification to be presented at the cardiac measurement equipment or a remote terminal, wherein the biophysical-signal data set is used to assess feature values in a model configured to non-invasively estimate presence of an expected disease state or condition, and wherein an estimated value is subsequently outputted for use in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition.
2 . The method of claim 1 , wherein the rejection of the biophysical signal data set triggers a reacquisition of a replacement biophysical signal data set from the subject with the cardiac measurement equipment.
3 . The method of claim 1 , wherein the step of determining the one or more values associated with the heart cycle variability parameter comprises:
determining, by the one or more processors, a template-signal vector data set representing quasi-periodic signal pattern of the subject from a plurality of detected quasiperiodic cycles detected in the biophysical-signal data set; and
applying, by the one or more processors, the template-signal vector data set to two or more of the plurality of detected quasiperiodic cycles to determine a residue, the one or more values associated with the heart cycle variability parameter being generated from the determined residue.
4 . The method of claim 1 , wherein the one or more values associated with a heart cycle variability parameter, and predefined variability threshold, are each a cycle variability score defined as an average of difference between a template-signal vector data set and two or more of a plurality of detected quasiperiodic cycles.
5 . The method of claim 1 , wherein the biophysical signal data set comprises three or more channels of acquired biopotential signals, and wherein the one or more values associated with the heart cycle variability parameter are generated as a respective score for a given channel normalized by a sum of scores of two or more channels of the acquired biopotential signals.
6 . The method of claim 1 , wherein the biophysical signal data set comprises two or more channels of an acquired biopotential signal data set or two or more channels of an acquired optical signal data set associated with the photoplethysmographic signal, including a first signal and a second signal, wherein the one or more values associated with the heart cycle variability parameter are determined as a volume-associated value of a phase space model of a residue generated between a template-signal vector data set and the first signal and the second signal.
7 . The method of claim 6 , wherein the phase space model is a triangulation point-cloud model generated from a difference between the template-signal vector data set and, at least, the first signal and the second signal.
8 . The method of claim 1 , wherein the one or more values associated with the heart cycle variability parameter are distribution-associated values of a difference between a template-signal vector data set and two or more of a plurality of detected quasiperiodic cycles.
9 . The method of claim 1 , wherein the biophysical signal data set comprises two or more channels of acquired biopotential signals, and wherein the one or more values associated with the heart cycle variability parameter are generated for each of the two or more channels of the acquired biopotential signals.
10 . The method of claim 1 , wherein the biophysical signal data set comprises two or more channels of a acquired optical signal data set associated with the photoplethysmographic signal or the cardiac signal, and wherein the one or more values associated with the heart cycle variability parameter are generated for each of two or more channels of acquired biopotential signals.
11 . The method of claim 1 , further comprising:
causing, by the one or more processors and/or remotely by the one or more cloud-based services or systems, a notification message associated with the rejection to be transmitted to the cardiac measurement equipment or a remote terminal.
12 . The method of claim 1 , further comprising:
determining, by the one or more processors and/or remotely by the one or more cloud-based services or systems, one or more synchronicity dynamical propert(ies) between a first biophysical signal data set associated with saturation of oxygenated and/or deoxygenated hemoglobin and a second biophysical signal data set associated with the cardiac signal; and
determining, by the one or more processors and/or remotely by the one or more cloud-based services or systems, an estimated value for presence of a disease state based on the determined one or more synchronicity dynamical propert(ies).
13 . The method of claim 1 , further comprising:
determining, by the one or more processors and/or remotely by the one or more cloud-based services or systems, one or more signal quality parameters of obtained first and second biophysical-signal data sets, wherein at least one of the one or more signal quality parameters is selected from group consisting of powerline interference parameter associated with powerline noise contamination, a high-frequency noise parameter associated with high frequency noise contamination, a noise burst parameter associated with high frequency noise burst contamination, an abrupt movement parameter associated with abrupt movement contamination, and an asynchronous noise parameter associated with skeletal muscle contamination.
14 . The method of claim 13 , wherein the step to determine the one or more signal quality parameters comprises:
calculating one or more biopotential noise scores of the biophysical signal data set,
wherein the one or more biopotential noise scores comprises a score value associated with biopotential powerline interference or a score value associated with biopotential high-frequency noise.
15 . The method of claim 13 , wherein the step to determine the one or more signal quality parameters comprises:
calculating one or more PPG noise scores of the biophysical signal data set, wherein the one or more PPG noise scores comprises at least one of:
a score value associated with a frequency of PPG signal jumps and dropouts;
a score value associated with a frequency of PPG signal that is clipped or railing, and
a score value associated with a duration of the PPG signal that is clipped or railing.
16 . The method of claim 1 , wherein the disease state includes significant coronary artery disease or abnormal left ventricular end-diastolic pressure.
17 . A system comprising:
a processor; and
a memory having instructions stored therein, wherein execution of the instructions by the processor, cause the processor to:
obtain a biophysical signal data set of a subject associated with a photoplethysmographic signal or a cardiac signal, wherein the biophysical signal data set is acquired from the subject with a cardiac measurement equipment of an instrument measurement system, wherein the biophysical signal data set comprises two or more channels of an acquired biopotential signal data set associated with the cardiac signal acquired by a first set of probes of the instrument measurement system, or two or more channels of an acquired optical signal data set associated with the photoplethysmographic signal acquired by a second set of probes of the instrument measurement system;
determine one or more values associated with a heart cycle variability parameter using the obtained cardiac signal and/or the photoplethysmographic signal of the biophysical-signal data set; and
reject the biophysical signal data set by one or more cloud-based services or systems, when the one or more values associated with the heart cycle variability parameter exceeds a predefined variability threshold, wherein the rejection generates a notification to be presented cardiac measurement equipment or a remote terminal, wherein the biophysical-signal data set is used to assess feature values in a model configured to non-invasively estimate presence of an expected disease state or condition, and wherein an estimated value is subsequently outputted for use in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition.
18 . A computer readable medium having instructions stored therein, wherein execution of the instructions by a processor, cause the processor to:
obtain a biophysical signal data set of a subject associated with a photoplethysmographic signal or a cardiac signal, wherein the biophysical signal data set is acquired from the subject with a cardiac measurement equipment of an instrument measurement system and wherein the biophysical signal data set comprises two or more channels of an acquired biopotential signal data set associated with the cardiac signal acquired by a first set of probes of the instrument measurement system, or two or more channels of an acquired optical signal data set associated with the photoplethysmographic signal acquired by a second set of probes of the instrument measurement system;
determine one or more values associated with a heart cycle variability parameter using the obtained cardiac signal and/or the photoplethysmographic signal of the biophysical-signal data set; and
reject the biophysical signal data set by one or more cloud-based services or systems when the one or more values associated with a heart cycle variability exceeds a predefined variability threshold, wherein the rejection generates a notification to be presented cardiac measurement equipment or a remote terminal, wherein the biophysical-signal data set is used to assess feature values in a model configured to non-invasively estimate presence of an expected disease state or condition, and wherein an estimated value is subsequently outputted for use in a diagnosis of the expected disease state or condition or to direct treatment of the expected disease state or condition.