SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Background and Objective:

Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages.

Methods:

In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals.

Results:

We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively.

Conclusions:

The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

SleepGCN:基于图卷积网络的睡眠分期过渡规则学习模型。
背景和目的:自动睡眠分期对于评估和诊断睡眠障碍至关重要,可为数百万睡眠障碍患者提供服务。最近提出了许多睡眠分期模型,但其中大多数都没有充分探讨睡眠过渡规则,而睡眠过渡规则对于睡眠专家识别睡眠阶段至关重要。因此,本文的目标之一就是开发一种自动睡眠分期模型,以捕捉睡眠阶段之间的过渡规则:本文提出了一种名为 SleepGCN 的新型睡眠分期模型。它利用睡眠表征学习(SRL)模块提取的脑电图(EEG)和脑电图(EOG)信号的深度特征,结合睡眠转换规则学习(STRL)模块学习到的转换规则来识别睡眠阶段。具体来说,SRL 模块利用残差网络(ResNet)和长短期记忆(LSTM)结构,从双通道脑电图-眼电图中捕捉每个睡眠阶段的深层时变特征和时间信息,然后应用特征增强模块获得细化特征。STRL模块采用图卷积网络(GCN)和过渡规则矩阵,根据输入信号的序列标签捕捉睡眠阶段之间的过渡规则:我们在五个公共数据集上对 SleepGCN 进行了评估:结果:我们在五个公开数据集上对 SleepGCN 进行了评估:SleepEDF-20、SleepEDF-78、SHHS、DOD-H 和 DOD-O。总体而言,SleepGCN 在这些数据集上的准确率分别为 89.70%、87.70%、86.16%、82.07% 和 81.20%,宏观平均 F1 分数分别为 85.20%、82.70%、77.69%、72.44% 和 72.93%:我们提出的模型所取得的结果远远优于所有其他比较模型。消融研究验证了 SleepGCN 中提出的 SRL 和 STRL 模块对睡眠分期任务的贡献。此外,研究还表明,使用双通道 EEG-EOG 的睡眠分期模型优于使用单通道 EEG 或 EOG 的模型。总之,SleepGCN 是使用双通道 EEG-EOG 进行睡眠分期的有效解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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