ArXiv Preprint
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 research, 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
architecture 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.
Zhenyuan Lu, Burcu Ozek, Sagar Kamarthi
2023-03-13
