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ECG heartbeat classification based on an improved ResNet-18 model

Date
2021
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%.
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Description
peer-reviewed
Publisher
Hindawi Publishing Corporation
Citation
Computational and Mathematical Methods in Medicine;6649970
Collections
Electronic & Computer Engineering
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Ji_2021_ECG.pdf
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Keywords
ResNet-18
ULRR Identifiers
https://hdl.handle.net/10344/10066
 https://doi.org/10.34961/3985
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Funding Information
Science and Technology Ministry of China, Bulgarian National Science Fund
Sustainable Development Goals
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