Multi-class SVM based on sleep stage identification using EEG signal

Currently, sleep disorders are considered as one of the major human life issues. Human sleep is a regular state of rest for the body in which the eyes are not only usually closed, but also have several nervous centers being inactive; hence, rendering the person either partially or completely unconscious and making the brain a less complicated network. This paper introduces an efficient technique towards differentiating sleep stages to assist physicians in the diagnosis and treatment of related sleep disorders. The idea is based on easily implementable filters in any hardware device and feasible discriminating features of the Electroencephalogram EEG signal by employing the one-against-all method of the multiclass Support Vector machine (SVM) to recognize the sleep stages and identify if the acquired signal is corresponding to wake, stage1, stage2, stage3 or stage4. The experimental results on several subjects achieve 92% of classification accuracy of the proposed work. A comparison of our proposed technique with some recent available work in the literature also presents the high classification accuracy performance.