Sleep staging from single-channel EEG with multi-scale feature and contextual information.

Journal: Sleep & breathing = Schlaf & Atmung
Published Date: Mar 12, 2019

Abstract

PURPOSE: Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which extracts sleep EEG features by multi-scale convolutional neural networks (CNN) and then infers the type of sleep stages by capturing the contextual information between adjacent epochs using recurrent neural networks (RNN) and conditional random field (CRF).

Authors

  • Kun Chen
    Department of Anesthesiology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
  • Cheng Zhang
    College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
  • Jing Ma
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Guangfa Wang
    Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, 100034, China.
  • Jue Zhang
    Wuhan University Zhongnan Hospital, Wuhan 430071, China.

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