Sleep Staging Framework with Physiologically Harmonized Sub-Networks
Copyright © 2022 Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:209 |
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| Enthalten in: |
Methods (San Diego, Calif.) - 209(2023) vom: 01. Jan., Seite 18-28 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Zheng [VerfasserIn] |
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| Links: |
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| Themen: |
EEG |
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| Anmerkungen: |
Date Completed 12.01.2023 Date Revised 02.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ymeth.2022.11.003 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349496668 |
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| 520 | |a Copyright © 2022 Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a EEG | |
| 650 | 4 | |a Half-precision training | |
| 650 | 4 | |a Mixed neural network | |
| 650 | 4 | |a Sleep stage scoring | |
| 700 | 1 | |a Yang, Ziwei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Ono, Naoaki |e verfasserin |4 aut | |
| 700 | 1 | |a Altaf-Ul-Amin, M D |e verfasserin |4 aut | |
| 700 | 1 | |a Kanaya, Shigehiko |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Ming |e verfasserin |4 aut | |
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