Automatic Sleep Scoring System Based On Autoregressive Model

Electroencephalography (EEG) signals recorded during sleep are divided into 30-second segments and analyzed by experts and classified into sleep stages, and in this way, diagnosis and treatment of diseases are made. Computer-assisted sleep scoring systems can effectively reduce the burden of experts by classifying sleep stages. In this study, after preprocessing of the EEG signals, four features were obtained for each EEG band (20 features in total) with the autoregressive model and then classified with three different classifiers common in machine learning. The proposed method was tested on the data set called ISRUC sleep (Subgroup 3, ISRUC3) and the results were compared with the results of other studies. The results show that the proposed method is more successful than other existing methods, despite the use of fewer features.