Efficient Sleep Stage Classification Based on EEG Signals
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Authors
Aboalayon, Khald A.I.
Ocbagabir, Helen
Faezipour, Miad
Issue Date
2014-03-28
Type
Presentation
Language
en_US
Keywords
Engineering , Faculty research day , Sleep stage classification , Electroencephalography (EEG)
Alternative Title
Abstract
Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. In this work, Butterworth band-pass filters are designed to filter and decompose the Electroencephalogram signal (EEG) into five sub-bands δ, Ɵ, α, β and γ. In addition, various discriminating features including energy, standard deviation, entropy are computed and extracted from above frequency sub-bands. The features are then fed to a supervised learning classifier; support vector machine (SVM) to be able to recognize the sleep stages and identify if the acquired signal is corresponding to awake or stage 1. The experimental results on a variety of subjects verify the high classification accuracy of the proposed work with 92.5 %.