Article: Sleep stage based sleep disorder detection using single-channel electroencephalogram Journal: International Journal of Nanotechnology (IJNT) 2022 Vol.19 No.6/7/8/9/10/11 pp.1075 - 1090 Abstract: The use of artificial intelligence in healthcare is the next generation of healthcare which can be the bridge between physician and patient. Sleep disorders hamper one's performance and are considered as a serious problem to overcome. There are several sleep disorders like excessive sleep (hypersomnia), inability to sleep (insomnia), snoring, apnea, etc. are detected with the analysis of polysomnogram (PSG) signals. Considering many signals for PSG increases the system's memory and computation requirements. Hence, in this work a machine learning model is proposed, considering single-channel electroencephalogram (EEG). Unique features are defined and extracted from sleep stage data to detect sleep disorders. The entire system consists of the sleep stage detection followed by sleep disorder detection. An accuracy of 98.8% was obtained using an SVM classifier for sleep stage detection and an accuracy of 95.9% for sleep disorder detection using Ensemble bagged tree classifier. Inderscience Publishers - linking academia, business and industry through research

Title: Sleep stage based sleep disorder detection using single-channel electroencephalogram

Authors: Vijayakumar Gurrala; Padmasai Yarlagadda; Padmaraju Koppireddi

Addresses: Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana – 500090, India ' Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana – 500090, India ' Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh – 533003, India

Abstract: The use of artificial intelligence in healthcare is the next generation of healthcare which can be the bridge between physician and patient. Sleep disorders hamper one's performance and are considered as a serious problem to overcome. There are several sleep disorders like excessive sleep (hypersomnia), inability to sleep (insomnia), snoring, apnea, etc. are detected with the analysis of polysomnogram (PSG) signals. Considering many signals for PSG increases the system's memory and computation requirements. Hence, in this work a machine learning model is proposed, considering single-channel electroencephalogram (EEG). Unique features are defined and extracted from sleep stage data to detect sleep disorders. The entire system consists of the sleep stage detection followed by sleep disorder detection. An accuracy of 98.8% was obtained using an SVM classifier for sleep stage detection and an accuracy of 95.9% for sleep disorder detection using Ensemble bagged tree classifier.

Keywords: apnea detection; human computer interface; machine learning; single channel sleep EEG; sleep stages; sleep disorders; wavelet decomposition.

DOI: 10.1504/IJNT.2022.128988

International Journal of Nanotechnology, 2022 Vol.19 No.6/7/8/9/10/11, pp.1075 - 1090

Received: 13 Aug 2021
Accepted: 30 Dec 2021
Published online: 13 Feb 2023 *

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Title: Sleep stage based sleep disorder detection using single-channel electroencephalogram

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