Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning

[2608.14662] Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14662 (eess) [Submitted on 1 Aug 2026] Title: Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning Authors: Dominika Kunc , Przemysław Kazienko , Stanisław Saganowski View a PDF of the paper titled Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning, by Dominika Kunc and 2 other authors View PDF HTML (experimental) 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. Comments: 5 pages, 3 Figures, 1 Table, appear in the Proceedings of the 13th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW 2025) Subjects: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: 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 Submission history From: Dominika Kunc [ view email ] [v1] Sat, 1 Aug 2026 01:09:16 UTC (748 KB) Full-text links: Access Paper: View a PDF of the paper titled Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning, by Dominika Kunc and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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