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Physiological responses to pain have received increasing attention amongresearchers for developing an automated pain recognition sensing system. Thoughless explored, Blood Volume Pulse (BVP) is one of the candidate physiologicalmeasures that could help objective pain assessment. In this study, we appliedmachine learning techniques on BVP signals to device a non-invasive modalityfor pain sensing. Thirty-two healthy subjects participated in this study.First, we investigated a novel set of time-domain, frequency-domain andnonlinear dynamics features that could potentially be sensitive to pain. Theseinclude 24 features from BVP signals and 20 additional features from Inter-beatIntervals (IBIs) derived from the same BVP signals. Utilizing these features,we built machine learning models for detecting the presence of pain and itsintensity. We explored different machine learning models, including LogisticRegression, Random Forest, Support Vector Machines, Adaptive Boosting(AdaBoost) and Extreme Gradient Boosting (XGBoost). Among them, we found thatthe XGBoost offered the best model performance for both pain classification andpain intensity estimation tasks. The ROC-AUC of the XGBoost model to detect lowpain, medium pain and high pain with no pain as the baseline were 80.06 %,85.81 %, and 90.05 % respectively. Moreover, the XGboost classifierdistinguished medium pain from high pain with ROC-AUC of 91%. For themulti-class classification among three pain levels, the XGBoost offered thebest performance with an average F1-score of 80.03%. Our results suggest thatBVP signal together with machine learning algorithms is a promisingphysiological measurement for automated pain assessment. This work will have anational impact on accurate pain assessment, effective pain management,reducing drug-seeking behavior among patients, and addressing national opioidcrisis.

Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques