Jang Eun-Hye, Eum Young-Ji, Yoon Daesub, Byun Sangwon
Mobility User Experience Research Section, Electronics Telecommunication and Research Institute, Daejeon, Republic of Korea.
Department of Psychology, Chungnam National University, Daejeon, Republic of Korea.
Sci Rep. 2025 Jul 29;15(1):27674. doi: 10.1038/s41598-025-12476-8.
Accurate pain assessment is essential for effective management; however, most studies have focused on differentiating pain from non-pain or estimating pain intensity rather than distinguishing between distinct pain types. We present a machine learning method for classifying physical and social pain using physiological signals. Seventy-three healthy adults participated in experiments involving baseline, neutral, and pain-inducing stimuli related to both types of pain. Physical pain was elicited by pressure cuff inflation, whereas social pain was induced by watching a video depicting a loved one's death. The electrocardiogram, electrodermal activity, photoplethysmogram, respiration, and finger temperature were recorded, and 12 physiological features were extracted. Three machine learning algorithms-logistic regression, support vector machine, and random forest-were employed to classify the input data into baseline versus painful states and physical versus social pain. Our findings demonstrated high accuracy in identifying social pain (0.82) and physical pain (0.90) compared to the baseline. Classification accuracy between physical and social pain was moderate (0.63) when using painful state data alone but improved to 0.77 when incorporating reactivity from neutral to painful states. This study highlights the potential of multimodal physiological signals for differentiating pain types and enhancing personalized pain management strategies.
准确的疼痛评估对于有效管理至关重要；然而，大多数研究都集中在区分疼痛与非疼痛或估计疼痛强度上，而不是区分不同类型的疼痛。我们提出了一种使用生理信号对身体疼痛和社交疼痛进行分类的机器学习方法。73名健康成年人参与了涉及与两种疼痛相关的基线、中性和疼痛诱发刺激的实验。身体疼痛通过压力袖带充气诱发，而社交疼痛则通过观看描绘亲人死亡的视频诱发。记录心电图、皮肤电活动、光电容积脉搏波、呼吸和手指温度，并提取12个生理特征。采用三种机器学习算法——逻辑回归、支持向量机和随机森林——将输入数据分类为基线状态与疼痛状态以及身体疼痛与社交疼痛。我们的研究结果表明，与基线相比，识别社交疼痛（0.82）和身体疼痛（0.90）的准确率很高。仅使用疼痛状态数据时，身体疼痛和社交疼痛之间的分类准确率中等（0.63），但当纳入从中性状态到疼痛状态的反应性时，准确率提高到0.77。这项研究突出了多模态生理信号在区分疼痛类型和加强个性化疼痛管理策略方面的潜力。
