Application of Heuristic Feature Selection in EEG based Sleep Stages Classification

Sleep is a natural and essential state for all beings. By examining people's sleep, various mental and physical diseases such as depression, apnea, and epilepsy can be diagnosed. Due to this fact, classification of sleep stages is very important and critical in sleep analysis. In this paper, an automatic method is proposed for sleep stages classification based on EEG signal. To do this, different statistical, entropy, and distance features are extracted from one-channel EEG of sleep-EDF database. To select the optimum set of features, different heuristic optimization algorithms such as genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA) are exploited. Using the proposed method, two to six classes of sleep are classified by 97.54%-92.66% accuracy which, shows its superiority over classic methods.