Abstract
The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.
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Acknowledgements
This work was supported by the National Natural Science Foundation for Young Scholars of China (Grant No. 61403276), Tianjin Research Program of Application Foundation and Advanced Technology (14JCYBJC42400).
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The authors (Ran Wei, Xinghua Zhang, Jinhai Wang, Xin Dang) declare that they have no conflict of interests in relation to the work in this article.
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Approval was obtained from the CSULB Institutional Review Board for experiments involving human subjects.
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Wei, R., Zhang, X., Wang, J. et al. The research of sleep staging based on single-lead electrocardiogram and deep neural network. Biomed. Eng. Lett. 8, 87–93 (2018). https://doi.org/10.1007/s13534-017-0044-1
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DOI: https://doi.org/10.1007/s13534-017-0044-1