Title: A novel single channel EEG-based sleep stage classification using SVM

Authors: Vijayakumar Gurrala; Padmasai Yarlagadda; Padmaraju Koppireddi

Addresses: Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India ' Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India ' Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh, India

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%.

Keywords: single channel EEG; sleep stage classification; SSC; machine learning classification; support vector machine; SVM.

DOI: 10.1504/IJBET.2021.116112

International Journal of Biomedical Engineering and Technology, 2021 Vol.36 No.2, pp.119 - 132

Received: 31 Jan 2020
Accepted: 31 Mar 2020
Published online: 12 Jul 2021 *

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