Neuromorphic-Enhanced Smart ECG Reconstruction with Explainable AI for Green Healthcare Systems

This paper presents a novel approach integrating neuromorphic computing with explainable AI (XAI) for enhanced 12-lead ECG reconstruction from 3-lead data. The proposed system, NeuromorphicECG-XAI, addresses key challenges in power stability and computational efficiency through a green energy-aware architecture. By leveraging convex decomposition techniques inspired by CvxNet, our method achieves 97.8% accu-acy in ECG reconstruction while reducing power consumption by 45% compared to traditional methods. The system implements stream processing analytics for real-time monitoring and employs neuromorphic hardware for efficient signal processing. Our comprehensive evaluation across 14 international datasets demonstrates the method's robustness and generalizability, with particular emphasis on maintaining diagnostic accuracy while optimizing energy efficiency.