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However, the current subjective pain assessment highly depends on the patient\u2019s response and is therefore unreliable. In this paper, we propose a physiological-signals-based objective pain recognition method that can extract new features, which have never been discovered in pain detection, from electrodermal activity (EDA) and electrocardiogram (ECG) signals. To discriminate the absence and presence of pain, we establish four classification tasks and build four tree-based classifiers, including Random Forest, Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and TabNet. The comparative experiments demonstrate that our method using the EDA and ECG features yields accurate classification results. 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