1. Sleep Staging Framework with Physiologically Harmonized Sub-Networks.
- Author
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Chen, Zheng, Yang, Ziwei, Wang, Dong, Zhu, Xin, Ono, Naoaki, Altaf-Ul-Amin, M.D., Kanaya, Shigehiko, and Huang, Ming
- Subjects
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ARTIFICIAL neural networks , *SLEEP stages , *DROWSINESS , *BRAIN waves , *FEATURE extraction , *EDGE computing - Abstract
• This paper proposes a reasonable sleep stage scoring framework, where we take into account the physiological characteristics of brain waves. • A novel sleep data processing is proposed to automate the sleep scoring task. By wrapping up the sub-networks that are responsible for learning the transient and overall characteristics of EEG during sleep with Transformer, a parallel model for sequence, the performance is improved when compared against the baseline models. • Considering the practicality in a real clinical setting, this paper adopts a mixed-precision strategy to speed up and alleviate the resource cost. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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