用可解释模型分析生理信号定位疼痛，发现皮肤电活动特征最有效。
An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
- 对比手工特征与深度学习模型在疼痛定位中的表现
- 最佳模型准确率53.9%，比深模型高7.4个百分点
- 适合临床非语言患者疼痛评估研究者参考
自动疼痛定位旨在从外周生理信号中识别疼痛的解剖位置，无需患者自我报告，对非语言患者具有重要临床意义。本文基于AI4Pain 2026挑战赛数据集，系统比较经典特征工程与深度序列学习方法，针对65名受试者在经皮神经电刺激（TENS）诱导下的四类可穿戴信号（皮肤电活动、血容量脉搏、呼吸、外周氧饱和度）进行无主体三分类疼痛定位。构建了包含时域、频域、模态特异及跨模态描述符的115维手工特征集，并与端到端深度模型对比。极端随机树（Extremely Randomized Trees）取得最高宏平均F1值0.539，优于最佳深度模型7.4个百分点；皮肤电活动频域特征成为主要判别依据。所有模型均显示疼痛检测（F1=0.815）与定位（F1=0.552）间存在26个百分点的稳定差距，揭示在10秒分辨率下，外周自主神经通路的解剖弥散性构成根本性能天花板。
原文摘要 · Abstract (English)
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.
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