Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier

Sleep efficiency measures provide an objective assessment to gauge the quality of individual's sleep. In this study we present a home-based, automated and non-intrusive system that is based on Electrocardiogram (ECG) measurements and uses a multi-stage Support Vector Machines (SVM) classifier to measure three indices for sleep quality assessment per 30 s epoch segment: Sleep Efficiency Index, Delta-Sleep Efficiency Index and Sleep Onset Latency. This method provides an alternative to the intrusive and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method.