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Article

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

1
Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India
2
School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
4
School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(10), 1141; https://doi.org/10.3390/e22101141
Received: 23 August 2020 / Revised: 2 October 2020 / Accepted: 5 October 2020 / Published: 9 October 2020
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications II)
The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application. View Full-Text
Keywords: entropy; sleep stages; multi-channel EEG; MPFBEWT; accuracy entropy; sleep stages; multi-channel EEG; MPFBEWT; accuracy
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MDPI and ACS Style

Tripathy, R.K.; Ghosh, S.K.; Gajbhiye, P.; Acharya, U.R. Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals. Entropy 2020, 22, 1141. https://doi.org/10.3390/e22101141

AMA Style

Tripathy RK, Ghosh SK, Gajbhiye P, Acharya UR. Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals. Entropy. 2020; 22(10):1141. https://doi.org/10.3390/e22101141

Chicago/Turabian Style

Tripathy, Rajesh K., Samit K. Ghosh, Pranjali Gajbhiye, and U. R. Acharya 2020. "Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals" Entropy 22, no. 10: 1141. https://doi.org/10.3390/e22101141

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