[论文解读] Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques

Fatemeh Pouromran, Yingzi Lin+1 more|arXiv (Cornell University)|Mar 19, 2023

Pain Mechanisms and TreatmentsMedicine被引用 3

一句话总结

本研究提出了一种基于光电容积脉搏波（BVP）信号和机器学习的自动化疼痛识别系统，从BVP和R-R间期（IBI）中提取44项特征（时域、频域及非线性动力学特征），用于分类疼痛的存在与否及疼痛强度。XGBoost模型表现最佳，对低、中、重度疼痛与无疼痛的区分AUC分别为80.06%、85.81%和90.05%，对中度与重度疼痛的区分AUC达91%。

ABSTRACT

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.

研究动机与目标

• 开发一种基于BVP信号的非侵入性、自动化客观疼痛评估系统。
• 识别对疼痛敏感的BVP和R-R间期（IBI）相关生理特征集合。
• 评估多种机器学习模型在疼痛存在与否及强度水平分类中的表现。
• 确定最优模型以支持临床疼痛管理并减少阿片类药物滥用。

提出的方法

• 在32名健康受试者中收集受控疼痛诱导下的BVP信号。
• 提取24项BVP特异性特征（时域、频域及非线性动力学特征）和20项从IBI中提取的特征。
• 训练并比较五种机器学习模型：逻辑回归、随机森林、支持向量机（SVM）、AdaBoost和XGBoost。
• 使用10折交叉验证评估模型在疼痛分类和强度估计中的性能。
• 采用网格搜索和贝叶斯优化对每种模型的超参数进行优化。
• 通过ROC-AUC、F1-score和混淆矩阵评估多分类与二分类任务的性能。

实验结果

研究问题

• RQ1 BVP信号及其衍生的IBI特征能否有效捕捉与疼痛相关的生理变化？
• RQ2 哪种机器学习模型在基于BVP衍生特征的疼痛存在与强度分类中表现最佳？
• RQ3 该系统在区分低、中、重度疼痛与无疼痛方面准确度如何？
• RQ4 该模型能否以高敏感性和特异性区分中度与重度疼痛？
• RQ5 非线性动力学特征与频域特征对疼痛识别性能的贡献如何？

主要发现

• XGBoost在区分重度疼痛与无疼痛时达到最高的ROC-AUC（90.05%），优于所有其他模型。
• XGBoost在区分中度疼痛与无疼痛时AUC为85.81%，在区分低度疼痛与无疼痛时AUC为80.06%。
• 在中度与重度疼痛的二分类任务中，XGBoost达到91%的AUC，表明其具有强大的区分能力。
• XGBoost在三类疼痛水平（低、中、高）的多分类任务中，平均F1-score达到80.03%。
• 与仅使用BVP特征相比，引入非线性动力学特征和IBI衍生特征显著提升了模型性能。
• XGBoost在所有疼痛强度水平下均表现出鲁棒性和良好的泛化能力，是自动化疼痛识别的最优模型。