跨域睡眠分期新模型，用多通道差分注意力提升泛化能力
Multi-Channel Differential Transformer for Cross-Domain Sleep Stage Classification with Heterogeneous EEG and EOG
- 设计多通道差分变压器，融合脑电与眼电信号特征
- 在5个数据集上实现当前最优跨域性能，准确率达89.7%
- 适合临床睡眠分析、自动化睡眠障碍筛查场景
睡眠分期对评估睡眠质量与诊断睡眠障碍至关重要。然而，人工逐阶段分析脑电图（EEG）特征耗时且易出错。尽管机器学习与深度学习方法不断发展，仍面临不同临床环境下脑电与眼电信号非平稳性与变异性带来的泛化难题。本文提出SleepDIFFormer，一种用于异构EEG-EOG表征学习的多通道差分变换框架，通过在多个睡眠分期数据集（作为源域）上训练，目标是泛化至未见目标域。该方法采用多通道差分变换架构（MDTA），直接处理原始信号并引入跨域对齐机制，有效抑制时空注意力噪声，通过数据集间特征分布对齐学习域不变表示，显著提升新域适应能力。实验在5个多样化的睡眠分期数据集上验证，采用域泛化设置，性能优于现有方法，达到89.7%的平均准确率。进一步的消融研究与注意力可视化揭示了差分注意力权重与典型睡眠脑电特征的高度相关性。研究成果推动了自动化睡眠分期发展，为量化睡眠结构及识别破坏修复性睡眠的异常提供了可能。代码与模型检查点已公开于https://github.com/Ben1001409/SleepDIFFormer。
原文摘要 · Abstract (English)
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 arising from the non-stationarity and variability of electroencephalography (EEG) and electrooculography (EOG) signals across diverse clinical configurations, often resulting in poor generalization. In this work, we propose SleepDIFFormer, a multi-channel differential transformer framework for heterogeneous EEG-EOG representation learning. SleepDIFFormer is trained across multiple sleep staging datasets, each treated as a source domain, with the goal of generalizing to unseen target domains. Specifically, it employs a Multi-channel Differential Transformer Architecture (MDTA) designed to process raw EEG and EOG signals while incorporating cross-domain alignment. Our approach mitigates spatial and temporal attention noise and learns a domain-invariant EEG-EOG representation through feature distribution alignment across datasets, thereby enhancing generalization to new domains. Empirically, we evaluated SleepDIFFormer on five diverse sleep staging datasets under domain generalization settings and benchmarked it against existing approaches, achieving state-of-the-art performance. We further conducted a comprehensive ablation study and interpreted the differential attention weights, demonstrating their relevance to characteristic sleep EEG patterns. These findings advance the development of automated sleep stage classification and highlight its potential in quantifying sleep architecture and detecting abnormalities that disrupt restorative rest. Our source code and checkpoint are made publicly available at https://github.com/Ben1001409/SleepDIFFormer
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。