ArticleFrontiers in physiology2023
Transformer encoder with multiscale deep learning for pain classification using physiological signals.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- AI-enabled wireless wearable breathing sensor for breathing pattern recognition.Scientific reports · 2026Article
- Implementation of a Stress Biomarker and Development of a Deep Neural Network-Based Multi-Mental State Classification Model.Bioengineering (Basel, Switzerland) · 2025Article
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
- Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.JMIR biomedical engineering · 2025Article
- A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion.Scientific reports · 2025Article
- Classification accuracy of pain intensity induced by leg blood flow restriction during walking using machine learning based on electroencephalography.Scientific reports · 2025Article
- A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.PloS one · 2025Article
- Pseudo-labeling based adaptations of pain domain classifiers.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Article
- Transformer encoder with multiscale deep learning for pain classification using physiological signals.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model's superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.
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