EEG-Based Automatic Sleep Stage Classification

Abstract

Sleep disorders have a great impact in the patients’ quality of life. The study of human sleep during the different sleep stages is crucial in the diagnosis of sleep disorders and is mainly performed with polysomnography (PSG). In this work, a methodology for sleep staging using solely Electroencephalographic (EEG) signals from PSG recordings is presented. EEG signals from the ISRUC-Sleep dataset are selected and used, aiming to automatically identify the five sleep stages. Initially, the EEG signal is filtered in order to extract the five EEG rhythms and the energy is calculated in each sub-band and used to train several typical classifiers. Results in terms of classification accuracy reached 75.29% with Random Forests.