Machine-Learning Based Classification of Sleep-Wake States using a Respeck Patch

This paper describes an unobtrusive sleep-wake monitor based on the wireless Respeck sensor worn as a patch on the chest, coupled with machine learning-based classification methods for the automatic classification of Sleep-Wake states in the Respeck sensor data. The \Sleep-Wake monitor was evaluated on labelled data collected by a cohort of 20 healthy volunteers and achieved results for accuracy, sensitivity and specificity of greater than 90% using Linear SVM, and non-linear SVM-RBF models. Metrics such as frequency of Wake-After-Sleep-Onset (WASO) episodes were calculated using Respeck data to mark the quality of sleep in the cohort.