Pain is a serious worldwide health problem that affects a vast proportion of the population. For efficient pain management and treatment, accurate classification and evaluation of pain severity are necessary. However, this can be challenging as pain is a subjective sensation-driven experience. Traditional techniques for measuring pain intensity, e.g. self-report scales, are susceptible to bias and unreliable in some instances. Consequently, there is a need for more objective and automatic pain intensity assessment strategies. In this paper, we develop PainAttnNet (PAN), a novel transfomer-encoder deep-learning framework for classifying pain intensities with physiological signals as input. The proposed approach is comprised of three feature extraction architectures: multiscale convolutional networks (MSCN), a squeeze-and-excitation residual network (SEResNet), and a transformer encoder block. On the basis of pain stimuli, MSCN extracts short- and long-window information as well as sequential features. SEResNet highlights relevant extracted features by mapping the interdependencies among features. The third module employs a transformer encoder consisting of three temporal convolutional networks (TCN) with three multi-head attention (MHA) layers to extract temporal dependencies from the features. Using the publicly available BioVid pain dataset, we test the proposed PainAttnNet model and demonstrate that our outcomes outperform state-of-the-art models. These results confirm that our approach can be utilized for automated classification of pain intensity using physiological signals to improve pain management and treatment.

翻译：疼痛是一个影响全球大量人口的严重健康问题。为实现有效的疼痛管理和治疗，需对疼痛强度进行准确分类与评估。然而，由于疼痛是一种主观感知驱动的体验，这一过程颇具挑战性。传统疼痛强度测量方法（如自我报告量表）易受偏差影响，且在某些情况下不可靠。因此，亟需更客观、自动化的疼痛强度评估策略。本文提出PainAttnNet（PAN），一种新型基于Transformer编码器的深度学习框架，以生理信号为输入对疼痛强度进行分类。该方法包含三个特征提取模块：多尺度卷积网络（MSCN）、压缩激励残差网络（SEResNet）和Transformer编码器模块。MSCN基于疼痛刺激提取短窗口与长窗口信息及序列特征；SEResNet通过映射特征间的相互依赖关系突出相关提取特征；第三模块采用由三个时序卷积网络（TCN）和三个多头注意力（MHA）层构成的Transformer编码器，从特征中提取时间依赖性。我们使用公开的BioVid疼痛数据集测试所提出的PainAttnNet模型，结果表明其性能优于现有最优模型。这些结果证实，该方法可基于生理信号实现对疼痛强度的自动化分类，从而改善疼痛管理与治疗。