Key result
DL-ECG Net targets cutting CPU inference latency ~94% vs Cycle-GAN for real-time arrhythmia classification.
Why the study?
Generative Adversarial Network-based architectures achieve high offline ECG arrhythmia classification accuracy, but they are typically too large and computationally heavy for direct deployment in low-latency clinical or web-based settings.
Does a knowledge-distilled lightweight CNN (DL-ECG Net) maintain high ECG arrhythmia classification accuracy while reducing computational overhead and CPU inference latency compared to a full Cycle-GAN classifier across the MIT-BIH and PTB-XL ECG datasets?
Does a knowledge-distilled lightweight CNN (DL-ECG Net) maintain high ECG arrhythmia classification accuracy while reducing computational overhead and CPU inference latency compared to a full Cycle-GAN classifier across the MIT-BIH and PTB-XL ECG datasets?
This paper proposes a conceptual two-stage deep learning architecture using knowledge distillation from a Cycle-GAN to a lightweight CNN to enable low-latency, web-based automated ECG arrhythmia screening, though actual implementation and validation have not yet been performed.
A Cardiac Arrhythmias remain a leading cause of sudden cardiac events worldwide, and timely automated screening from Electrocardiogram (ECG) signals can materially improve patient outcomes. Recent literature has explored Generative Adversarial Network (GAN) based architectures that achieve very high offline classification accuracy, but these designs are typically large, computationally heavy, and unsuitable for direct deployment in low-latency clinical or web-based settings. This work proposes an extension to that line of research: a two-stage pipeline in which a cycle-consistent generative model would be used only during the offline training phase to balance rare arrhythmia classes and to enrich the feature space, while a separate, knowledge-distilled lightweight Convolutional Neural Network (CNN) would be trained to reproduce the larger network's decision behavior using soft-label distillation. The distilled classifier is designed to be small enough to run interactively, wrapped inside a Python Flask web application that accepts a single ECG sample, returns the predicted arrhythmia class, and renders a bar chart of class-wise prediction confidence. This document presents the proposed architecture, the planned evaluation on the MIT-BIH Arrhythmia Data-base and the PTB-XL 12-lead ECG dataset, to benchmarking methodology predictions
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Asha V (2026) studied Cardiac arrhythmias (n=137,772). Distilled Lightweight ECG Classifier (DL-ECG Net) trained via soft-label knowledge distillation from an offline cycle-consistent GAN classifier vs. Offline Cycle-GAN classifier was evaluated on Projected CPU inference latency and computational efficiency (parameters, FLOPs, model size) alongside ECG arrhythmia classification performance. The conceptual DL-ECG Net framework targets reducing parameters from 5.0M to 0.31M and CPU latency from 186 ms to 11 ms compared to the Cycle-GAN classifier, awaiting experimental validation.
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