An Efficient Ventricular Arrhythmias Detection on Microcontrollers with Optimized 1D CNN

This paper introduces a novel solution utilizing a 1D convolutional neural network (CNN) with optimizations like adaptive max-pooling, point-wise convolution, and a multi-objective gradient reversal layer (GRL) to address real-time ventricular arrhythmia (VA) detection challenges in implantable cardioverter defibrillators (ICDs). The proposed model achieves exceptional accuracy in discerning VAs and Non-VAs from single-channel intracardiac electrogram (IEGM) signals, boasting a F_β score of 0.99265, a generalization score of 0.9375, a memory footprint of 24.332 KiB, and an inference latency of 2.593 ms. Compared to top models from the 2022 TinyML Design Contest, the proposed method demonstrates superior detection accuracy and generalization performance while maintaining competitive inference latency and memory usage.