Wong, David Liang TaiDavid Liang TaiWongLi, YongfuYongfuLiJohn, DeepuDeepuJohnHo, Weng KhuenWeng KhuenHoHeng, Chun-HuatChun-HuatHeng2024-08-132024-08-132022 IEEE2022-04IEEE Transactions on Biomedical Circuits and Systems1932-4545http://hdl.handle.net/10197/26554Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-W.en© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ElectrocardiographyCovolutionPregnancyHeat rate variabilityField programmable gate arraysConvolutional neural networksImage edge detectionJournal Article16222223210.1109/tbcas.2022.31526232022-02-19https://creativecommons.org/licenses/by-nc-nd/3.0/ie/