中文标题#
SleepDIFFormer：透過多變量微分 Transformer 進行睡眠階段分類
英文标题#
SleepDIFFormer: Sleep Stage Classification via Multivariate Differential Transformer
中文摘要#
睡眠階段的分類對於評估睡眠質量和診斷睡眠障礙至關重要。然而，對每個階段的腦電圖（EEG）特徵進行手動檢查既耗時又容易出錯。儘管機器學習和深度學習方法已被積極開發，但它們仍然面臨來自不同領域（即數據集）的腦電圖（EEG）和眼電圖（EOG）信號的非平穩性和變異性帶來的挑戰，通常導致泛化能力差。本工作提出了一種睡眠階段分類方法，通過開發多變量微分變換器（SleepDIFFormer）進行聯合 EEG 和 EOG 表示學習。具體來說，SleepDIFFormer 被開發用於使用我們的多變量微分變換器架構（MDTA）處理 EEG 和 EOG 信號，通過跨領域對齊進行訓練。我們的方法在學習領域不變的聯合 EEG-EOG 表示時減輕了空間和時間注意力噪聲，從而實現了對未見過的目標數據集的泛化。實證上，我們在五個不同的睡眠階段數據集上評估了我們的方法，並與現有方法進行了比較，取得了最先進的性能。我們還對 SleepDIFFormer 進行了全面的消融分析，並解釋了微分注意力權重，突出了它們與特徵睡眠 EEG 模式的相關性。這些發現對推動自動化睡眠階段分類及其在睡眠質量評估中的應用具有重要意義。我們的源代碼可在https://github.com/Ben1001409/SleepDIFFormer 公開獲取。
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：透過多變量微分 Transformer 進行睡眠階段分類
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