The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. This study uses BioVid Heat Pain Dataset. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors.
This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients.
In current clinical settings, typically pain is measured by a patient’s self-reported information.
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