Abstract
Electroencephalogram (EEG) is most acceptable in the field of sleep disorder analysis. It is a basic primary signal through which we monitor and diagnose the sleep-related diseases of the patients. The main objective of this study is to automatic sleep stage classification based on a single channel of EEG signals in gender-specific subjects. This proposed method followed certain steps to complete this study. In this study, we have considered four basic steps like (i) preprocessing, (ii) feature extraction, (iii) feature selection, and (iv) classification. Here, we have proposed a two-stage classification, and for this research work, we have selected one public dataset of sleep study named ISRUC-Sleep dataset which is collected from the Hospital of Coimbra University (CHUC) in the department of sleep in Portugal. In this study, we have considered a single channel of EEG signals with different gender subjects. In our proposed research work, the SVM classification techniques turned out to be most useful for classifying the sleep stages of subject-16 through the C4-A1 channel with an accuracy of 97.20% and kappa coefficient of 0.88, which indicates a substantial agreement with the gold standard. We have presented a novel comparison of channel effectiveness in the count to sleep scoring of sleep stages. Additionally, we also made a comparison between classification algorithm performances in this study, and the results make it more suitable for scientific and clinical sleep disorder assessment.
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Satapathy, S.K., Loganathan, D., Pattnaik, S., Rath, R. (2021). Automated Sleep Staging of Human Polysomnography Recordings Using Single-Channel of EEG Signals. In: Manik, G., Kalia, S., Sahoo, S.K., Sharma, T.K., Verma, O.P. (eds) Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-0942-8_17
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