A Light-Weight Deep Residual Network for Classification of Abnormal Heart Rhythms on Tiny Devices

Abstract

An automatic classification of abnormal heart rhythms using electrocardiogram (ECG) signals has been a popular research area in medicine. In spite of reporting good accuracy, the available deep learning-based algorithms are resource-hungry and can not be effectively used for continuous patient monitoring on portable devices. In this paper, we propose an optimized light-weight algorithm for real-time classification of normal sinus rhythm, Atrial Fibrillation (AF), and other abnormal heart rhythms using single-lead ECG on resource-constrained low-powered tiny edge devices. A deep Residual Network (ResNet) architecture with attention mechanism is proposed as the baseline model which is duly compressed using a set of collaborative optimization techniques. Results show that the baseline model outperforms the state-of-the art algorithms on the open-access PhysioNet Challenge 2017 database. The optimized model is successfully deployed on a commercial microcontroller for real-time ECG analysis with a minimum impact on performance.