Physiological responses to pain have received increasing attention among researchers for developing an automated pain recognition sensing system. Though less explored, Blood Volume Pulse (BVP) is one of the candidate physiological measures that could help objective pain assessment. In this study, we applied machine learning techniques on BVP signals to device a non-invasive modality for pain sensing. Thirty-two healthy subjects participated in this study. First, we investigated a novel set of time-domain, frequency-domain and nonlinear dynamics features that could potentially be sensitive to pain. These include 24 features from BVP signals and 20 additional features from Inter-beat Intervals (IBIs) derived from the same BVP signals. Utilizing these features, we built machine learning models for detecting the presence of pain and its intensity. We explored different machine learning models, including Logistic Regression, Random Forest, Support Vector Machines, Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost). Among them, we found that the XGBoost offered the best model performance for both pain classification and pain intensity estimation tasks. The ROC-AUC of the XGBoost model to detect low pain, medium pain and high pain with no pain as the baseline were 80.06 %, 85.81 %, and 90.05 % respectively. Moreover, the XGboost classifier distinguished medium pain from high pain with ROC-AUC of 91%. For the multi-class classification among three pain levels, the XGBoost offered the best performance with an average F1-score of 80.03%. Our results suggest that BVP signal together with machine learning algorithms is a promising physiological measurement for automated pain assessment. This work will have a national impact on accurate pain assessment, effective pain management, reducing drug-seeking behavior among patients, and addressing national opioid crisis.

翻译：疼痛引发的生理反应正日益受到研究者的关注，以开发自动化疼痛识别传感系统。尽管研究较少，血容量脉搏（BVP）是可能有助于客观疼痛评估的候选生理指标之一。本研究将机器学习技术应用于BVP信号，旨在构建一种无创的疼痛感知模态。32名健康受试者参与了本次实验。首先，我们探究了一组可能对疼痛敏感的时域、频域及非线性动力学新型特征，包括从BVP信号中提取的24个特征，以及从同一BVP信号导出的心搏间期（IBIs）的20个额外特征。利用这些特征，我们构建了用于检测疼痛存在及其强度的机器学习模型。我们探索了多种机器学习模型，包括逻辑回归、随机森林、支持向量机、自适应增强（AdaBoost）和极端梯度提升（XGBoost）。其中，XGBoost在疼痛分类和疼痛强度估计任务中均表现出最佳性能。以无痛为基线，XGBoost模型检测低痛、中痛和高痛的ROC-AUC分别为80.06%、85.81%和90.05%。此外，XGBoost分类器区分中痛与高痛的ROC-AUC达91%。在三个疼痛等级的多分类任务中，XGBoost以平均F1分数80.03%取得最佳性能。我们的结果表明，BVP信号结合机器学习算法是实现自动化疼痛评估的一种有前景的生理测量方法。该工作将对准确疼痛评估、有效疼痛管理、减少患者药物寻求行为以及应对国家阿片类药物危机产生全国性影响。