心脏能反映你的疼痛吗？用自监督心电表示学习应对X-ITE疼痛挑战赛
Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
- Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对X-ITE疼痛挑战赛，结合自监督心电表示学习与多模态预训练，分析疼痛识别中的心电信号特性，为可穿戴疼痛监测提供了基础。
AI中文摘要：
利用生理信号准确识别疼痛仍是一个具有挑战性的问题，因为疼痛具有主观性且个体间差异很大。本研究探究应用于单模态心电（ECG）的自监督表示学习（SSL）方法，并辅以多模态预训练，包括来自胸部的加速度计（ACC）信号。我们重点对X-ITE疼痛数据集中的低疼痛水平与中疼痛水平进行分类。结果显示，虽然基于ECG的模型分类性能有限，但多模态预训练能通过捕获跨模态依赖关系改善学习到的表示。值得注意的是，我们观察到模型性能存在显著的个体间差异，这表明与疼痛相关的ECG模式可能具有个体特异性。可视化结果显示出不同的个体特异性聚类，但未按疼痛水平明确分离，凸显了仅从ECG检测疼痛的复杂性。我们讨论了单模态输入、标签噪声和跨个体泛化的局限性，并提出了未来方向。这项工作加深了对用于疼痛识别的生理信号表示学习的理解，为更稳健、临床相关的可穿戴疼痛监测解决方案奠定了基础。
英文摘要：
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.
