Author/Authors :
Jing, Enbiao North China University of Science and Technology, China , Zhang, Haiyang Department of Computer Science - University of Sheffield, UK , Li, ZhiGang North China University of Science and Technology, China , Liu, Yazhi North China University of Science and Technology, China , Ji, Zhanlin North China University of Science and Technology, China , Ganchev, Ivan Department of Computer Systems - University of Plovdiv “Paisii Hilendarski” - Plovdiv, Bulgaria
Abstract :
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat
classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the
unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better
classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that
the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the
ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.
