Sleep Staging Framework with Physiologically Harmonized Sub-Networks

Zheng Chen, Ziwei Yang, Dong Wang, Xin Zhu, Naoaki Ono, M. D. Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)


Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

出版ステータス出版済み - 1月 2023

!!!All Science Journal Classification (ASJC) codes

  • 分子生物学
  • 生化学、遺伝学、分子生物学一般


「Sleep Staging Framework with Physiologically Harmonized Sub-Networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。