中文标题#
SleepDIFFormer：通过多变量微分 Transformer 进行睡眠阶段分类
英文标题#
SleepDIFFormer: Sleep Stage Classification via Multivariate Differential Transformer
中文摘要#
睡眠阶段的分类对于评估睡眠质量和诊断睡眠障碍至关重要。然而，对每个阶段的脑电图（EEG）特征进行手动检查既耗时又容易出错。尽管机器学习和深度学习方法已被积极开发，但它们仍然面临来自不同领域（即数据集）的脑电图（EEG）和眼电图（EOG）信号的非平稳性和变异性带来的挑战，通常导致泛化能力差。本工作提出了一种睡眠阶段分类方法，通过开发多变量微分变换器（SleepDIFFormer）进行联合 EEG 和 EOG 表示学习。具体来说，SleepDIFFormer 被开发用于使用我们的多变量微分变换器架构（MDTA）处理 EEG 和 EOG 信号，通过跨领域对齐进行训练。我们的方法在学习领域不变的联合 EEG-EOG 表示时减轻了空间和时间注意力噪声，从而实现了对未见过的目标数据集的泛化。实证上，我们在五个不同的睡眠阶段数据集上评估了我们的方法，并与现有方法进行了比较，取得了最先进的性能。我们还对 SleepDIFFormer 进行了全面的消融分析，并解释了微分注意力权重，突出了它们与特征睡眠 EEG 模式的相关性。这些发现对推动自动化睡眠阶段分类及其在睡眠质量评估中的应用具有重要意义。我们的源代码可在https://github.com/Ben1001409/SleepDIFFormer 公开获取。
英文摘要#
Classification of sleep stages is essential for assessing sleep quality and diagnosing sleep disorders. However, manual inspection of EEG characteristics for each stage is time-consuming and prone to human error. Although machine learning and deep learning methods have been actively developed, they continue to face challenges from the non-stationarity and variability of electroencephalography (EEG) and electrooculography (EOG) signals across different domains (i.e., datasets), often leading to poor generalization. This work proposed a Sleep Stage Classification method by developing Multivariate Differential Transformer (SleepDIFFormer) for joint EEG and EOG representation learning. Specifically, SleepDIFFormer was developed to process EEG and EOG signals using our Multivariate Differential Transformer Architecture (MDTA) for time series, trained with cross-domain alignment. Our method mitigated spatial and temporal attention noise while learning a domain-invariant joint EEG-EOG representation through feature distribution alignment, thereby enabling generalization to unseen target datasets. Empirically, we evaluated our method on five different sleep staging datasets and compared it with existing approaches, achieving state-of-the-art performance. We also conducted a thorough ablation analysis of SleepDIFFormer and interpreted the differential attention weights, highlighting their relevance to characteristic sleep EEG patterns. These findings have implications for advancing automated sleep stage classification and its application to sleep quality assessment. Our source code is publicly available at https://github.com/Ben1001409/SleepDIFFormer
文章页面#
SleepDIFFormer：通过多变量微分 Transformer 进行睡眠阶段分类
PDF 获取#
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