A Lightweight ML-Based ECG Classification System Using Self-Personalized Anomaly Detector

Abstract

Targeting the real-time arrhythmia diagnosis on resource-limited edge devices, in this paper, we present a lightweight electrocardiogram classification system using event-driven machine learning processing. A self-personalized anomaly detector based on signal processing is newly developed to dynamically update internal decision criteria from each patient's recent electrocardiogram history, that activates the following machine learning model only for the abnormal cases. A Siamese neural network is adopted to identify detailed arrhythmia classes by comparing features from the self-personalized normal data and the current abnormal input, increasing the classification accuracy. We also develop a simple version of our Siamese model to reduce the number of trainable parameters while preserving the end-to-end classification accuracy. Experimental results show that the proposed event-driven system reduces ML model activations by 74% for normal beats, achieving a classification accuracy of 96.9% comparable to leading solutions. Additionally, it consumes three times less energy and achieves 3.6 times faster processing latency compared to cost-aware method on a mobile GPU platform, enabling extended battery life and real-time analysis on edge devices.