TY - JOUR
T1 - ECG Heartbeat Classification Based on an Improved ResNet-18 Model
AU - Jing, Enbiao
AU - Zhang, Haiyang
AU - Li, Zhi Gang
AU - Liu, Yazhi
AU - Ji, Zhanlin
AU - Ganchev, Ivan
N1 - Publisher Copyright:
© 2021 Enbiao Jing et al.
PY - 2021
Y1 - 2021
N2 - 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%.
AB - 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%.
UR - https://www.scopus.com/pages/publications/85106151798
U2 - 10.1155/2021/6649970
DO - 10.1155/2021/6649970
M3 - Article
C2 - 34007306
AN - SCOPUS:85106151798
SN - 1748-670X
VL - 2021
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
M1 - 6649970
ER -