REAL-TIME ARRHYTHMIA DETECTION FROM WEARABLE ECG DEVICES USING LIGHTWEIGHT 1D CNN AND EDGE AI DEPLOYMENT

Cardiovascular diseases (CVDs) are still among the most prevalent causes of death globally, making it important to have quick, constant, and non-invasive cardiac monitoring. In this paper, a real-time system for arrhythmia detection using wearable ECG sensors combined with a low-power one-dimensional Convolutional Neural Network (1D-CNN) optimized for Edge AI deployment is introduced. The model is tailored to run efficiently on low-power wearables like smartwatches and ECG patches. A publicly available dataset, the MIT-BIH Arrhythmia Database, was used for training and validation. Preprocessing involved band-pass filtering (0.5–40 Hz), z-score normalization, and segmentation in 2-second windows. The 1D-CNN architecture with four convolutional blocks and a softmax classifier had an accuracy of 98.72 %, precision of 97.85 %, recall of 98.10 %, and F1-score of 0.981 on the test set. Model pruning and quantization-based edge optimization decreased memory consumption by 42 % and inference latency by 35 %, making it possible for real-time processing at 62 frames per second on the Raspberry Pi 4 platform. The system showed dependable skill in distinguishing different arrhythmias, including left bundle branch block (LBBB), atrial fibrillation (AF), and premature ventricular contraction (PVC). As a result, this pioneering method offers a technically low-power and readily scalable way to continuously monitor cardiac health, which can be used in both hospitals and patients homes.