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:疼痛是影响大量人口的严重全球性健康问题。为了有效的疼痛管理和治疗,需要准确分类和评估疼痛程度。然而,由于疼痛是一种主观感受驱动的经验,这可能具有挑战性。传统的疼痛强度测量技术,例如自我报告量表,在某些情况下容易出现偏见和不可靠。因此,需要更为客观和自动化的疼痛强度评估策略。在本文中,我们开发了一个名为PainAttnNet(PAN)的新型变压器编码器深度学习框架,用于使用生理信号作为输入进行疼痛分类。所提出的方法由三个特征提取架构组成:多尺度卷积网络(MSCN),squeeze-and-excitation残差网络(SEResNet)和Transformer编码器块。基于疼痛刺激,MSCN提取短窗口和长窗口信息以及顺序特征。SEResNet通过映射特征之间的相互依赖关系来突出相关提取的特征。第三个模块采用由三个时间卷积网络(TCN)和三个多头注意力(MHA)层组成的Transformer编码器,从特征中提取时间依赖性。通过使用公开可用的BioVid疼痛数据集,我们测试了所提出的PainAttnNet模型,并证明了我们的结果优于现有最先进的模型。这些结果证实,我们的方法可用于使用生理信号自动分类疼痛强度,以改善疼痛管理和治疗。