Journal
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
Volume 40, Issue 1, Pages 494-504Publisher
ELSEVIER
DOI: 10.1016/j.bbe.2020.01.010
Keywords
Sleep stages; Continuous wavelet transform; Deep learning; Convolution neural network; Transfer learning
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For automatic sleep stage classification, the existing methods mostly rely on hand-crafted features selected from polysomnographic records. In this paper, the goal is to develop a deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time-frequency spectrum of EEG signal, removing the need for manual feature extraction. The time-frequency RGB color images for EEG signal are extracted using continuous wavelet transform (CWT). The transfer learning of a pre-trained convolution neural network, squeezenet is employed to classify these CWT images into its sleep stages. The proposed method is evaluated using a publicly available Physionet sleep EDFx dataset using single-channel EEG Fpz-Cz channel. Evaluation results show that this method can achieve near state of the art accuracy even using single channel EEG signal. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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