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

TL;DR

This paper introduces PainAttnNet, a novel deep learning framework combining multiscale convolution, residual networks, and transformer encoders to objectively classify pain intensity from physiological signals, outperforming existing models.

Contribution

The paper presents a new transformer-based deep learning model for pain classification using physiological signals, integrating multiscale feature extraction and attention mechanisms.

Findings

Outperforms state-of-the-art models on BioVid dataset

Effective in extracting temporal dependencies from physiological signals

Demonstrates potential for automated pain assessment

Abstract

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.