Deep learning-aided polar-low density parity check decoding for enhanced telemedicine ECG transmission reliability

Abstract

Telemedicine has emerged as a crucial solution for remote patient monitoring and diagnosis, yet ensuring the reliable transmission of medical data, particularly electrocardiogram (ECG) signals, remains a significant challenge. This work proposes a novel approach that integrates deep learning with a polar-low density parity check (LDPC) decoder to enhance the accuracy, robustness, and efficiency of ECG signal transmission within telemedicine systems. The study aims to evaluate the effectiveness of this integration in improving error correction and decoding performance, validate its efficacy under diverse signal to noise ratios (SNRs) and code rates, and assess its potential impact on remote healthcare delivery. Experimental results confirm that the deep learning-empowered polar-LDPC decoder achieves superior error correction and decoding efficiency compared to conventional methods, ensuring higher fidelity in ECG reconstruction. This advancement presents a promising pathway toward more reliable, precise, and efficient telemedicine systems, thereby enabling improved patient care, especially in remote and underserved regions. The proposed method also opens opportunities for integrating intelligent decision-support tools. Such integration could further enhance real-time diagnostics and broaden telemedicine’s scope.