TOWARDS AN AUTOMATIC PAIN INTENSITY LEVELS EVALUATION FROM MULTIMODAL PHYSIOLOGICAL SIGNAL USING MACHINE LEARNING APPROACHES (Nigerian Journal of Technology, 2025)
A pain assessment is necessary in order to identify and manage pain. Self-report has been the prime method of measuring intensity of pain. To address this, an impartial methodology to recognizing pain that is both scalable and inexpensive must be developed. In this study, a Bio-Vid Heat Pain Database (Part A) dataset containing 86 individuals in good health condition who experience extreme pain was utilized to develop algorithms for pain recognition. Two physiological indicators, electrocardiogram and electrodermal activity were utilized. Different kinds of machine learning algorithms were implemented to establish the framework for more advances in the development of complex pain classification algorithms. CatBoost and AdaBoost performed significantly better than other methods, with average performance accuracy of 83.68% and 82.68% respectively for fusion of electrocardiogram and electrodermal activity signals. The binary classification experiment discriminates between the baseline and the pain tolerance level (T0 vs. T4).