System and methods for screening obstructive sleep apnea during wakefulness using anthropometric information and tracheal breathing sounds

A method for improving computational analysis of physiological signals to derive a machine-learning obstructive sleep apnea (OSA) screening tool from a heterogeneous initial dataset containing confounding anthropometric variables, said method comprising:

• (a) storage, in non-transitory computer readable memory, of an initial dataset that comprises, for each of a plurality of human test subjects from whom respective audio recordings were taken during periods of wakefulness, a respective subject dataset in which there is stored at least:
• a known OSA severity parameter of the test subject;
• anthropometric data identifying different anthropometric parameters of the test subject;
• and
• audio data containing stored audio signals from said respective audio recording of said test subject;
• (b) extraction, by said computer, of at least spectral and bi-spectral features from the audio data of the subject datasets;
• (c) selection of a training dataset from said initial dataset, and from said training dataset, grouping together the subject datasets from a first high-severity group of said test subjects whose known OSA severity parameters are above a first threshold, and grouping together the subject datasets of a second low-severity group of said test subjects whose known OSA severity parameters are below a second threshold that is lesser than said first threshold;
• (d) based on the anthropometric data, division of the subject datasets from each of the high-severity and low-severity groups into a plurality of anthropometrically distinct subsets, among which the subject data sets in each subset share a common confounding anthropometric variable, thereby partitioning the heterogeneous initial dataset for targeted modeling;
• (e) derivation, by said computer, of input for a classifier training procedure at least partly by:
• (i) for each anthropometrically distinct subset, filtering said extracted spectral and bi-spectral features down to a selected subset of features;
• (ii) for each anthropometrically distinct subset, creating a respective pool of predictive models for predicting OSA severity values, of which each predictive model in said pool is based on a different combination of features from the selected subset of features;
• and
• (iii) for each anthropometrically distinct subset, evaluating the predictive models of the respective pool against one another to reduce the respective pool down to a reduced subset of the predictive models, by, for each predictive model in the pool:
• (1) dividing a response variable of the model into a plurality of severity segments;
• (2) calculating a unique correlation value for each of the plurality of severity segments to account for regional non-linearity;
• (3) determining an average of the calculated correlation values;
• and (4) filtering the pool of predictive models to generate the reduced subset based on said average of the calculated correlation values;
• (f) using said derived input, training by said computer of a classifier for the purpose of classifying a human patient as either OSA or non-OSA based on a respective patient dataset that contains at least:
• anthropometric data identifying different anthropometric parameters of the patient;
• and
• at least one of either:
• audio data containing stored audio signals from a respective audio recording of said patient during a period of full wakefulness;
• and/or
• feature data concerning features already extracted from said audio signals from the respective audio recording of said patient;
• and
• (g) storage in the same or another non-transitory computer readable memory:
• said trained classifier;
• for each anthropometrically distinct subset, identification of a respective feature set, from among said extracted spectral and bi-spectral features, that is to be used for classification of patients whose anthropometric parameters overlap those of the subject datasets of said anthropometrically distinct subset;
• and
• statements and instructions executable by one or more computer processors to:
• read the anthropometric data of the patient dataset;
• match the anthropometric data of the patient dataset to one or more of the anthropometrically distinctive subsets to identify one or more respective feature sets associated therewith;
• based on identification of said one or more respective feature sets, select which particular features are required from the audio data or feature data of the patient dataset;
• and
• input said particular features to said trained classifier to classify said patients as either OSA or non-OSA.