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Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification Publisher Pubmed



Mohammadi E1 ; Makkiabadi B2 ; Shamsollahi MB3 ; Reisi P4 ; Kermani S5
Authors
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Authors Affiliations
  1. 1. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
  4. 4. Department of Physiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  5. 5. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: International Journal of Neural Systems Published:2022


Abstract

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification. © 2022 World Scientific Publishing Company.
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