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EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis

EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis

Basavaraj Hiremath, Natarajan Sriraam, B. R. Purnima, Nithin N. S., Suresh Babu Venkatasamy, Megha Narayanan
Copyright: © 2021 |Volume: 12 |Issue: 6 |Pages: 18
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799867517|DOI: 10.4018/IJEHMC.20211101.oa2
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MLA

Hiremath, Basavaraj, et al. "EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis." IJEHMC vol.12, no.6 2021: pp.1-18. http://doi.org/10.4018/IJEHMC.20211101.oa2

APA

Hiremath, B., Sriraam, N., Purnima, B. R., Nithin N. S., Venkatasamy, S. B., & Narayanan, M. (2021). EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-18. http://doi.org/10.4018/IJEHMC.20211101.oa2

Chicago

Hiremath, Basavaraj, et al. "EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.6: 1-18. http://doi.org/10.4018/IJEHMC.20211101.oa2

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Abstract

Electroencephalogram (EEG) signals resulting from recordings of polysomnography play a significant role in determining the changes in physiology and behavior during sleep. This study aims at demarcating the sleep patterns of yogic and non-yogic subjects. Frequency domain features based on power spectral density methods were explored in this study. The EEG recordings were segmented into 1s and 0.5s. EEG patterns with four windowing scheme overlaps (0%, 50%, 60% and 75%) to ensure stationarity of the signal in order to investigate the effect of the pre-processing stage. In order to recognize the yoga and non-yoga group through N3 sleep stage, non-linear KNN classifier was introduced and performance was evaluated in terms of sensitivity and specificity. The experimental results show that modified covariance PSD estimate is the best method in classifying the sleep stage N3 of yogic and non-yogic subjects with 95% confidence interval, sensitivity, specificity and accuracy of 97.3%, 98% and 97%, respectively.