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Automated Classification of Sleep Stages Using Single-Channel EEG: A Machine Learning-Based Method

Santosh Kumar Satapathy, D. Loganathan

Source Title: International Journal of Information Retrieval Research (IJIRR)12(2)

ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182092|DOI: 10.4018/IJIRR.299941

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MLA

Satapathy, Santosh Kumar, and D. Loganathan. "Automated Classification of Sleep Stages Using Single-Channel EEG: A Machine Learning-Based Method." IJIRR vol.12, no.2 2022: pp.1-19. http://doi.org/10.4018/IJIRR.299941

APA

Satapathy, S. K. & Loganathan, D. (2022). Automated Classification of Sleep Stages Using Single-Channel EEG: A Machine Learning-Based Method. International Journal of Information Retrieval Research (IJIRR), 12(2), 1-19. http://doi.org/10.4018/IJIRR.299941

Chicago

Satapathy, Santosh Kumar, and D. Loganathan. "Automated Classification of Sleep Stages Using Single-Channel EEG: A Machine Learning-Based Method," International Journal of Information Retrieval Research (IJIRR) 12, no.2: 1-19. http://doi.org/10.4018/IJIRR.299941

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Abstract

The main contribution of this paper is to present a novel approach for classifying the sleep stages based on optimal feature selection with ensemble learning stacking model using single-channel EEG signals.To find the suitable features from extracted feature vector, we obtained the ReliefF (ReF), Fisher Score (FS) and Online Stream Feature Selection (OSFS) selection algorithms.The proposed research work was performed on two different subgroups of sleep data of ISRUC-Sleep dataset. The experimental results of the proposed methodology signify that single-channel of EEG signal superior to other machine learning classification models with overall accuracies of 97.93%, 97%, and 95.96% using ISRUC-Sleep subgroup-I (SG-I) data and similarly the proposed model achieved an overall accuracies of 98.16%, 98.78%, and 95.26% using ISRUC-Sleep subgroup-III (SG-III) data with FS, ReF and OSFS respectively.