Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Journal: Computer methods and programs in biomedicine
PMID: 31586788

Abstract

BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, the state-of-the-art of such methods are quite complex. Using a simple CNN architecture to classify sleep stages is important for portable sleep devices. In addition, employing CNNs to learn rich and diverse representations remains a challenge. Therefore, we propose a novel CNN model for sleep stage classification.

Authors

  • Junming Zhang
  • Ruxian Yao
    College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China.
  • Wengeng Ge
    College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China.
  • Jinfeng Gao
    College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China. Electronic address: hhgaostudy@163.tom.

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