  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.
