A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture

摘要

Background: Sleep scoring is a critical step in medical researches and clinical applications. Traditional visual scoring method is based on the processing of physiological signals, such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG), which is a time consuming and subjective procedure. It is an urgent task to develop an effective method for automatic sleep scoring.