Prediction of sleep apnea episodes from a wireless wearable multisensor suite

Obstructive sleep apnea (OSA) is a common sleep disorder found in 24% of adult men and 9% of adult women. Although continuous positive airway pressure (CPAP) has emerged as a standard therapy for OSA, a majority of patients are not tolerant to this treatment due to the uncomfortable nasal air delivery during their sleep. We introduce a Dirichlet process Gaussian Mixture (DPGM) model to predict the occurrence of sleep apnea episodes based on analyzing complex cardiorespiratory signals gathered from a custom-designed wireless wearable multisensory suite. Extensive testing with PhysioNet´s OSA database suggests that the accuracy of offline OSA classification is 88%. Accuracies for predicting an OSA episode 1-min ahead is 83% and 3-min ahead is 77%. Such accurate prediction of an impending OSA episode can be used to adaptively adjust CPAP airflow (towards improving the patients´ adherence), or the torso posture (e.g., minor chin adjustments to maintain the steady levels of airflow).