Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.

Journal: The International journal of neuroscience
Published Date: Nov 24, 2025

Abstract

AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an accurate and computationally efficient framework for multi-level neuropathic pain detection using electroencephalography signals. METHODS: A Quantum-Inspired Pyramid Depthwise Separable Residual Network is proposed, which integrates three innovations: a depthwise separable Residual Network to reduce computational complexity, a pyramid attention mechanism to capture multi-scale patterns, and a quantum-inspired transformation layer to model complex nonlinear dependencies among Electroencephalogram features. RESULTS: Experiments conducted on benchmark electroencephalography datasets confirm that the proposed model gains a accuracy of 99.65%, with a recall of 98.00%. CONCLUSION: The proposed model provides a reliable solution for objective neuropathic pain detection in post-stroke patients. The framework demonstrates potential for integration into intelligent clinical decision-support and brain-computer interface-based rehabilitation systems.

Authors

  • Sofia Bobby J
    Biomedical Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India.
  • Sheeja V Francis
    Electronics & Communication Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India.
  • Subha Ramya V
    Biomedical Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India.
  • Annapoorani C L
    Biomedical Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India.

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