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Pain is a serious worldwide health problem that affects a vast proportion ofthe population. For efficient pain management and treatment, accurateclassification and evaluation of pain severity are necessary. However, this canbe challenging as pain is a subjective sensation-driven experience. Traditionaltechniques for measuring pain intensity, e.g. self-report scales, aresusceptible to bias and unreliable in some instances. Consequently, there is aneed for more objective and automatic pain intensity assessment strategies. Inthis paper, we develop PainAttnNet (PAN), a novel transfomer-encoderdeep-learning framework for classifying pain intensities with physiologicalsignals as input. The proposed approach is comprised of three featureextraction architectures: multiscale convolutional networks (MSCN), asqueeze-and-excitation residual network (SEResNet), and a transformer encoderblock. On the basis of pain stimuli, MSCN extracts short- and long-windowinformation as well as sequential features. SEResNet highlights relevantextracted features by mapping the interdependencies among features. The thirdmodule employs a transformer encoder consisting of three temporal convolutionalnetworks (TCN) with three multi-head attention (MHA) layers to extract temporaldependencies from the features. Using the publicly available BioVid paindataset, we test the proposed PainAttnNet model and demonstrate that ouroutcomes outperform state-of-the-art models. These results confirm that ourapproach can be utilized for automated classification of pain intensity usingphysiological signals to improve pain management and treatment.

Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals