A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG...

Area of Science:

• Biomedical Engineering
• Quantum Computing
• Machine Learning

Background:

• Pain assessment relies on subjective reporting, leading to variability.
• Objective pain assessment using physiological signals like ECG is crucial.
• Current methods lack robust, privacy-preserving, and accurate pain classification.

Purpose of the Study:

• To develop a privacy-preserving framework for accurate pain level assessment using ECG signals.
• To integrate edge computing, quantum transfer learning, and federated learning for enhanced pain detection.
• To leverage Continuous Wavelet Transform (CWT) and Quantum Convolutional Hybrid Neural Networks (QCHNN) for improved analysis.

Main Methods:

• ECG signals were transformed into 2D CWT images to capture pain-induced cardiac variations.
• A quantum-classical hybrid architecture with edge computing for preprocessing and feature extraction was employed.
• A Quantum Convolutional Hybrid Neural Network (QCHNN) was trained using a federated learning framework for privacy preservation.

Main Results:

• The QCHNN achieved a classification accuracy of 94.8% for pain level assessment (low, medium, high).
• The framework demonstrated superior performance compared to traditional machine learning approaches.
• The integrated system effectively preserved patient privacy through secure aggregation protocols.

Conclusions:

• The proposed framework offers a robust and accurate method for objective pain assessment using ECG signals.
• The integration of edge computing, quantum transfer learning, and federated learning revolutionizes pain level classification.
• This research paves the way for advanced, privacy-conscious healthcare solutions in pain management.