Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation


  • Ruixuan Chen Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan
  • Linfeng Sui Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan
  • Mo Xia Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan
  • Jinsha Liu Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan
  • Tao Zhang College of Life Sciences, Nankai University, Tianjin, China
  • Jianting Cao Graduate School of Engineering, Saitama Institute of Technology, Fukaya 369-0217, Japan



k-fold cross-validation, convolutional neural networks, data augmentation, deep sleep


Sleep is a necessary process that individuals undergo daily for physical recovery, and the proportion of deep sleep in the sleep stages is a critical aspect of the recovery process. Convolutional Neural Networks (CNNs) have shown remarkable success in automatically identifying deep sleep stages through the analysis of electroencephalogram (EEG) signals. This article introduces three data augmentation techniques, including time shifting, amplitude scaling and noise addition, to enhance the diversity and features of the data. These techniques aim to enable machine learning models to extract features from various aspects of sleep EEG data, thus improving the model’s accuracy. Three deep learning models are introduced, namely DeepConvNet, ShallowConvNet and EEGNet, for the identification of deep sleep. To evaluate the proposed methods, the Sleep-EDF public dataset was utilized. Experimental results demonstrate that the enhanced dataset formed by applying the three data augmentation techniques achieved higher accuracy in all deep learning models compared to the original dataset. This highlights the feasibility and effectiveness of these methods in deep sleep detection.


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How to Cite

Chen, R. ., Sui, L. ., Xia, M. ., Liu, J. ., Zhang, T., & Cao, J. . (2024). Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 24, 1–13.



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