A novel single channel EEG-based sleep stage classification using SVM Online publication date: Mon, 12-Jul-2021
by Vijayakumar Gurrala; Padmasai Yarlagadda; Padmaraju Koppireddi
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 36, No. 2, 2021
Abstract: To do functions properly for the whole day our body needs quality sleep. Since each and every function is controlled by our brain, the study of EEG makes sense for the analysis of sleep issues. In sleep, everyone go past at max of six stages. Sleep stage classification (SSC) is the golden technique for evaluation of human sleep. The objective of this undertaking is to classify the sleep stages as a way to allow/help physicians to come across the sleep issues. We consider only the single EEG instead of multiple/multi-channel signals consider by the earlier works. Hence we name our method as single channel-SSC (SC-SSC). In this approach, we consider time domain as well as frequency domain features and the experimental machine learning classification - support vector machine (SVM) which results better performs to earlier methods. The proposed method of SC-SSC tested on sleep-EDF database and achieves an accuracy of 97.4%.
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