Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG

脑电图 计算机科学 人工智能 睡眠阶段 阶段(地层学) 睡眠(系统调用) 模式识别(心理学) 语音识别 机器学习 自然语言处理 心理学 多导睡眠图 神经科学 生物 操作系统 古生物学
作者
Chen Zhao,Wei Wu,Haoyi Zhang,Ruiyan Zhang,Xinyue Zheng,Xiangzeng Kong
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (12): 7068-7077 被引量:2
标识
DOI:10.1109/jbhi.2024.3432633
摘要

Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlabeled data. However, traditional SSL methods typically focus on single-view learning and do not fully exploit the interactions among information across multiple views. In this study, we focused on a multi-domain view of the same EEG signal and developed a self-supervised multi-view representation learning framework via time series and time-frequency contrasting (MV-TTFC). In the MV-TTFC framework, we built-in a cross-domain view contrastive learning prediction task to establish connections between the temporal view and time-frequency (TF) view, thereby enhancing the information exchange between multiple views. In addition, to improve the quality of the TF view inputs, we introduced an enhanced multisynchrosqueezing transform, which can create high energy concentration TF image views to compensate for the inaccurate representations in traditional TF processing techniques. Finally, integrating temporal, TF, and fusion space contrastive learning effectively captured the latent features in EEG signals. We evaluated MV-TTFC based on two real-world SSC datasets (SleepEDF-78 and SHHS) and compared it with baseline methods in downstream tasks. Our method exhibited state-of-the-art performance, achieving accuracies of 78.64% and 81.45% with SleepEDF-78 and SHHS, respectively, and macro F1-scores of 70.39% with SleepEDF-78 and 70.47% with SHHS.
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