ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Enbiao Jing, Haiyang Zhang, Zhi Gang Li, Yazhi Liu, Zhanlin Ji*, Ivan Ganchev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

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%.

Original languageEnglish
Article number6649970
JournalComputational and Mathematical Methods in Medicine
Volume2021
DOIs
Publication statusPublished - 2021
Externally publishedYes

Cite this