用自监督学习分析心电图，发现疼痛信号具个体差异性。
Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
- 结合心电图与胸戴加速度计，做多模态自监督预训练。
- 单模态心电图分类效果差，但多模态提升表征能力。
- 不同人疼痛心电特征差异大，适合可穿戴疼痛监测研究。
由于疼痛的主观性和高个体差异性，基于生理信号的疼痛准确识别仍具挑战。本研究探索将自监督表示学习（SSL）应用于单一心电图（ECG），并引入来自胸部的加速度计（ACC）信号进行多模态预训练，目标是在X-ITE Pain数据集上区分低与中等疼痛水平。结果显示，仅使用心电图的模型分类性能有限，而多模态预训练通过捕捉跨模态依赖关系，显著改善了学习到的表征。值得注意的是，模型表现存在显著的个体间差异，提示疼痛相关的心电模式可能具有高度个体特异性。可视化分析显示，样本按个体聚类明显，但未按疼痛等级清晰分离，凸显仅从心电图检测疼痛的复杂性。本文讨论了单模态输入局限、标签噪声及跨个体泛化问题，并提出未来方向。该工作推进了生理信号表征学习在疼痛识别中的应用，为更鲁棒的临床可穿戴疼痛监测方案奠定基础。
原文摘要 · Abstract (English)
Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability. In this study, we investigate self-supervised representation learning (SSL) methods applied to unimodal electrocardiogram (ECG), complemented by multimodal pretraining, including accelerometer (ACC) signals from the chest. We focus on classifying low versus medium pain levels on the X-ITE Pain dataset. Our results reveal that while ECG-based models show limited classification performance, multimodal pretraining improves learned representations by capturing cross-modal dependencies. Notably, we observe substantial inter-subject variability in model performance, suggesting that pain-related ECG patterns may be subject-specific. Visualizations indicate distinct subject-specific clustering but no clear separation by pain levels, highlighting the complexity of pain detection from ECG alone. We discuss limitations of unimodal input, label noise, and generalization across subjects and propose future directions. This work advances the understanding of physiological signal representation learning for pain recognition and sets the stage for more robust, clinically relevant wearable pain monitoring solutions.
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