AI-enhanced ECG analysis for arrhythmia classification with edge device compatibility

Abstract

Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating efficient and accessible diagnostic tools for early detection. While deep learning models have demonstrated high accuracy in arrhythmia classification, their deployment in resource-limited clinical settings remains challenging due to computational constraints. This study addresses this gap by developing and validating lightweight deep learning models specifically optimized for edge device deployment, enabling real-time ECG analysis in remote and under-resourced healthcare environments. Utilizing the MIT-BIH Arrhythmia Dataset, which includes 48 half-hour ECG recordings from 47 individuals, we trained and evaluated 2 models: a standard Convolutional Neural Network (CNN) and a computationally efficient Depth-Wise Separable Convolutional Neural Network (DWCNN). Preprocessing involved segmenting ECG recordings into individual heartbeats and addressing class imbalance through downsampling, resulting in a balanced dataset of 21,186 images across 6 arrhythmia types. The DWCNN achieved competitive diagnostic performance (precision: 0.97, recall: 0.97) while utilizing 75% fewer parameters than the standard CNN (8.45 M vs. 34.08 M) and 65% less memory (8.4 MB vs. 24.1 MB). Critically, we demonstrate successful deployment on the Intel Neural Compute Stick 2 (NCS2), a resource-constrained edge device, achieving inference times of 4.09 ms/241.91FPS on CPU and 9.05 ms/109.79FPS on the NCS2 platform. This practical demonstration of real-time arrhythmia classification on low-power edge devices represents a significant advancement toward accessible cardiac diagnostics in point-of-care settings, remote monitoring applications, and resource-limited healthcare facilities where centralized computing infrastructure or specialist expertise may be unavailable.