An Exploratory Analysis of Pain Localization via Explainable Computational Modeling

Ioannis Kyprakis, Stefanos Gkikas, Eric Nichols, Yu Fang, Manolis Tsiknakis

Automatic pain localization, which involves identifying the anatomical origin of pain from peripheral physiological signals without patient self-report, is a clinically critical but largely unaddressed problem, particularly for non-verbal patients. This paper presents a systematic comparison of classical feature engineering and deep sequence learning for subject-independent three-class pain localization using the AI4Pain 2026 Challenge dataset, which comprises four synchronously recorded wearable modalities: electrodermal activity, blood volume pulse, respiration, and peripheral oxygen saturation recorded from 65 participants under controlled TENS-induced pain. A 115-dimensional hand-crafted feature set spanning time-domain, frequency-domain, modality-specific, and cross-modal descriptors is benchmarked against end-to-end deep architectures. Extremely Randomized Trees achieves the highest macro-F1 of 0.539, outperforming the best deep model by 7.4 percentage points, with EDA spectral features emerging as the dominant discriminators. A consistent 26-point gap between pain detection (F1\,=\,0.815) and localization (F1\,=\,0.552) across all models points to a fundamental ceiling imposed by the anatomical diffuseness of peripheral autonomic pathways at 10-second resolution.