Transformer encoder with multiscale deep learning for pain classification using physiological signals - Lu, Zhenyuan; Ozek, Burcu; Kamarthi, Sagar (Frontiers in Physiology, 2023)
Pain, a pervasive global health concern, affects a large segment of population worldwide.Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We hypothesized that PainAttnNet would outperform existing models in capturing temporal dependencies, a hypothesis confirmed by our evaluations. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluations on the BioVid heat pain dataset confirm the model's superior performance, establishing PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.