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This study applies self-supervised representation learning to electrocardiogram signals for pain classification, demonstrating that multimodal pretraining with accelerometer data yields better results than unimodal ECG alone. However, the research highlights significant inter-subject variability and a lack of clear separation between pain levels in visualizations, underscoring the current limitations of using cardiac patterns as standalone biomarkers for subjective states like pain. The work advances physiological signal processing but indicates that robust clinical deployment requires addressing generalization across diverse individuals before it can be considered viable for wearable monitoring solutions.
Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
arXiv:2608.14662v1 Announce Type: cross Abstract: 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.
Title:Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
Abstract: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.
Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2608.14662 [eess.SP]
(or arXiv:2608.14662v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2608.14662 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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