通过可解释计算建模对疼痛定位进行探索性分析
An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
浏览论文内容
中文总结 AI 辅助
针对自动疼痛定位问题，利用AI4Pain 2026挑战数据集，比较经典特征工程与深度序列学习用于独立于受试者的三类疼痛定位，极端随机树表现最佳，发现疼痛检测与定位存在差距，揭示外周自主神经通路解剖扩散性的基本上限。
中文摘要 AI 辅助
自动疼痛定位是指从外周生理信号中识别疼痛的解剖学起源，而无需患者自我报告，这在临床上至关重要但大多未得到解决，特别是对于非语言患者。本文使用AI4Pain 2026挑战数据集，对经典特征工程和深度序列学习进行系统比较，用于独立于受试者的三类疼痛定位。该数据集包含从65名参与者在受控经皮电刺激诱导疼痛下同步记录的四种可穿戴模式：皮肤电活动、血容量脉搏、呼吸和外周血氧饱和度。一个115维的手工特征集与端到端深度架构进行基准测试。极端随机树达到最高宏F1为0.539，比最佳深度模型高出7.4个百分点，皮肤电活动光谱特征成为主要判别因素。所有模型在疼痛检测（F1 = 0.815）和定位（F1 = 0.552）之间始终存在26分的差距，这表明在10秒分辨率下，外周自主神经通路的解剖扩散性存在基本上限。
英文摘要
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.
