基于多模态一致性的自监督对比学习框架,用于意识障碍患者的自动睡眠分期。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jahui Pan, Yangzuyi Yu, Man Li, Wanxin Wei, Shuyu Chen, Heyi Zheng, Yanbin He, Yuanqing Li
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引用次数: 0

摘要

睡眠是人类的一项基本活动,自动睡眠分期具有相当大的研究潜力。尽管针对睡眠分期提出的许多深度学习方法都表现出了显著的性能,但仍有一些挑战尚未解决,包括表征和泛化能力不足、多模态特征提取的局限性、标记数据的稀缺性以及对意识障碍(DOC)患者的实际应用受到限制。本文提出了基于多模态一致性的睡眠分期网络 MultiConsSleepNet。该网络由一个单模态特征提取器和一个多模态一致性特征提取器组成,旨在探索脑电图(EEG)和眼电图(EOG)的通用表征,并提取模态内和模态间特征的一致性。此外,针对临床实践中难以获得高质量标注数据却拥有大量非标注数据的现状,还设计了单模态和多模态一致性学习的自监督对比学习策略。它能有效缓解模型对标注数据的依赖,提高模型的普适性,从而有效迁移到 DOC 患者。在三个公开数据集上的实验结果表明,MultiConsSleepNet 在有限的标注数据下实现了最先进的睡眠分期性能,并有效利用了非标注数据,增强了其实际应用性。此外,该模型还在一个自收集的 DOC 数据集上取得了令人鼓舞的结果,为 DOC 患者的睡眠分期研究提供了一个新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multimodal Consistency-Based Self-Supervised Contrastive Learning Framework for Automated Sleep Staging in Patients with Disorders of Consciousness.

Sleep is a fundamental human activity, and automated sleep staging holds considerable investigational potential. Despite numerous deep learning methods proposed for sleep staging that exhibit notable performance, several challenges remain unresolved, including inadequate representation and generalization capabilities, limitations in multimodal feature extraction, the scarcity of labeled data, and the restricted practical application for patients with disorder of consciousness (DOC). This paper proposes MultiConsSleepNet, a multimodal consistency-based sleep staging network. This network comprises a unimodal feature extractor and a multimodal consistency feature extractor, aiming to explore universal representations of electroencephalograms (EEGs) and electrooculograms (EOGs) and extract the consistency of intra- and intermodal features. Additionally, self-supervised contrastive learning strategies are designed for unimodal and multimodal consistency learning to address the current situation in clinical practice where it is difficult to obtain high-quality labeled data but has a huge amount of unlabeled data. It can effectively alleviate the model's dependence on labeled data, and improve the model's generalizability for effective migration to DOC patients. Experimental results on three publicly available datasets demonstrate that MultiConsSleepNet achieves state-of-the-art performance in sleep staging with limited labeled data and effectively utilizes unlabeled data, enhancing its practical applicability. Furthermore, the proposed model yields promising results on a self-collected DOC dataset, offering a novel perspective for sleep staging research in patients with DOC.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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