Machine learning quality assessment of physiological signals
AssigneeSensority Ltd.
InventorsMaria KAMINSKY, Bar MORDEHAY, Dmitry GOLDENBERG
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Abstract
A method comprising receiving, as input, a plurality of PPG waveform signal segments; extracting, from each of the segments, a feature set representing the PPG waveform signal; at a training stage, training a machine learning model on a training set comprising: (i) the feature sets, and (ii) labels indicating a quality parameters associated with the PPG waveform signal in each of the PPG waveform signal segments; and at an inference stage, applying the trained machine learning model to at least one feature set extracted from at least one target PPG waveform signal segment, to determine a quality parameter of the at least one target PPG waveform signal.
Claims20
A system comprising:
- at least one hardware processor and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to:
- receive, as input, at least one signal waveform segment representing an optically-obtained physiological signal;
- extract, from said at least one waveform segment, at least one respective feature set;
- and
- apply a trained machine learning (ML) model to the at least one feature set to determine a quality parameter of said at least one signal waveform.
A method comprising:
- receiving, as input, a at least one signal waveforms segment representing an optically-obtained physiological signal;
- extracting, from said at least one waveform segment, at least one respective feature set;
- and
- applying a trained ML model to the at least one feature set to determine a quality parameter of said at least one signal waveform.
A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by at least one hardware processor to:
- receive, as input, a plurality of signal waveform segments, each representing an optically-obtained physiological signal;
- extract, from each of said segments, a feature set representing said respective signal waveform in said segment;
- at a training stage, train a machine learning model on a training set comprising:
- (i) said feature sets, and
- (ii) labels indicating a quality parameters associated with said signal waveform in each of said respective segments;
- and
- at an inference stage, apply said trained machine learning model to at least one feature set extracted from at least one target signal waveform segment, to determine a quality parameter of said at least one target signal waveform.
Dependencies are inferred from the claim text.