Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware

2026-05-10

AI Summary

This study addresses the high computational cost of existing deep learning models for electrocardiogram (ECG) automated diagnosis, which hinders deployment on resource-constrained devices. To overcome this limitation, the authors propose three lightweight CNN architectures—ParallelCNN, ParallelCNNew, and SimpleNet—that incorporate parallel spatiotemporal feature extraction, symmetric weight initialization, and integration of demographic metadata. A multi-task classification framework is developed alongside a unified efficiency metric that jointly evaluates AUC, model size, inference speed, and memory footprint. Experiments on 12-lead ECG datasets from Germany, China, and the United States demonstrate that the proposed models achieve a favorable trade-off between high diagnostic accuracy and low resource consumption across binary, multi-class, and multi-label tasks, highlighting their potential for real-time clinical deployment.

Abstract

Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential for automating ECG classification, many existing models rely on large, computationally intensive architectures that hinder practical deployment. In this paper, we present an empirical study of convolutional neural network (CNN) architectures, exploring tradeoffs between diagnostic accuracy and computational efficiency. We benchmark two established baselines: AttiaNet, a compact model composed of sequential temporal and spatial blocks, and DeepResidualCNN, the winning architecture of the 2021 PhysioNet/Computing in Cardiology Challenge. Building on these, we propose three lightweight models: (i) ParallelCNN, which employs dual temporal and spatial branches for parallel pattern extraction; (ii) ParallelCNNew, a variant with symmetric weight initialization for balanced feature learning; and (iii) SimpleNet, a streamlined architecture that jointly processes temporal and spatial dimensions. Our experiments span three publicly available 12-lead ECG datasets from Germany, China, and the United States, covering binary, multiclass, and multilabel classification tasks across diverse patient populations. We further evaluate the impact of integrating low-cost demographic metadata (age and sex) to improve performance with minimal overhead. To ensure fair comparison, we introduce a unified Efficiency Score that integrates model size, inference speed, memory usage, and AUC performance. By balancing diagnostic performance and efficiency, our models offer a scalable and viable foundation for next-generation AI systems in cardiovascular care.

Problem

• ECG interpretation
• real-time
• limited hardware
• computational efficiency
• neural architectures

Innovation

• lightweight CNN
• ECG interpretation
• computational efficiency
• parallel architecture
• Efficiency Score