An Exploratory Analysis of Pain Localization via Explainable Computational Modeling

2026-07-21

AI Summary

This study addresses the challenge of identifying pain location in nonverbal patients by systematically evaluating handcrafted feature engineering against end-to-end deep sequence models for cross-subject anatomical pain localization into three categories, using peripheral physiological signals from four wearable devices provided in the AI4Pain 2026 Challenge dataset. The work reveals, for the first time, a fundamental limitation in peripheral autonomic pathways for pain localization at a 10-second temporal resolution and identifies EDA spectral features as critical discriminative factors. A multi-modal handcrafted feature set of 115 dimensions combined with Extremely Randomized Trees achieves a macro-F1 score of 0.539, significantly outperforming the best deep learning model by 7.4%. Furthermore, the study uncovers a consistent performance gap of approximately 26 percentage points between pain detection and localization tasks.

Abstract

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.

Problem

• pain localization
• automatic pain detection
• non-verbal patients
• peripheral physiological signals
• anatomical origin of pain

Innovation

• pain localization
• explainable computational modeling
• feature engineering
• deep sequence learning
• physiological signals