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ECG heartbeat classification based on an improved ResNet-18 model

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posted on 2021-05-12, 10:23 authored by Enbiao Jing, Haiyang Zhang, ZhiGang Li, Yazhi Liu, Zhanlin Ji, Ivan GanchevIvan Ganchev
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%.

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Publication

Computational and Mathematical Methods in Medicine;6649970

Publisher

Hindawi

Note

peer-reviewed

Other Funding information

Science and Technology Ministry of China, Bulgarian National Science Fund

Language

English

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