Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stages classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stages classification can facilitate this process. In this work an attempt was made to classify six sleep stages consisting of Awake, Stage1, Stage2, Stage3, Stage4, and REMS. Spectral analysis, Wavelet transform and Fuzzy clustering based on fuzzy c-means algorithm (FCM) were deployed for this purpose. Twelve recordings of a healthy six stages studied per 30s epochs. The results demonstrated that the performance for automatically discriminated for these six sleep stages from each other when using wavelet packet with sym3 where the classification was with average 92.27%.