PainXtract: A Multimodal System for Multiclass Pain Classification Using Physiological Signals

Abstract

Pain is a complex sensory, cognitive, and emotional experience that plays a vital role in diagnosis, treatment, and patient monitoring. Pain detection is critical for effective care but remains challenging due to its subjective nature, especially in individuals unable to communicate verbally. Traditional visual and auditory cues, such as facial expressions or vocalizations, can vary significantly across cultures and contexts and may be consciously masked, limiting their reliability. Physiological signals, in contrast, offer objective and involuntary indicators of pain-related autonomic responses. We present PainXtract, a multimodal system submitted to the Second Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN 2025). PainXtract classifies pain levels: no pain, low pain, and high pain, using handcrafted morphological features from electrodermal activity (EDA), blood volume pulse (BVP), respiratory signals (Resp), and peripheral oxygen saturation (SpO<inf>2</inf>). PainXtract achieves 75.57% accuracy on multiclass classification and near-perfect performance on binary classification, demonstrating the value of physiological signals and the central role of EDA in pain assessment. We also conduct extensive studies to assess the contribution of each modality. Our system outperforms the baseline system on both the validation and testing sets. © 2025 Copyright held by the owner/author(s)