Sleep stage detection using sensor integration

Abstract

Sleep, once thought to form a passive process in human life, is recently discovered to be not so passive with respect to brain activity at all. It has been possible to diagnose both physiological and psychological disorders by the inspection of sleep stages. This paper presents a model for sleep stage detection using EEG, heart rate and galvanic skin response sensors that are affordable and easily accessible. Sleep stage detection system was tested on human subjects using support vector machine and has achieved accuracy of over 87% in sleep stage detection.