Speech as a Biomarker for Obstructive Sleep Apnea Detection

Abstract

Obstructive sleep apnea (OSA) is a prevalent sleep disorder, responsible for a decrease of people's quality of life, and significant morbidity and mortality associated with hypertension and cardiovascular diseases. OSA is caused by anatomical and functional alterations in the upper airways, thus we hypothesize that the speech properties of OSA patients are altered, making it possible to detect OSA through voice analysis. To address this hypothesis, we collected speech recordings from 25 OSA subjects and 20 controls, designed a feature set, and compared different machine learning algorithms for binary classification. We achieved a True-Positive-Rate of 88% and a True-Negative-Rate of 80% with a majority vote ensemble of SVM, LDA and kNN classifiers. These results were validated with in-the-wild data acquired from Youtube. Moreover, the negative impact of sleep disorders on working memory was also shown by the results obtained in one of the recorded verbal tasks.