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G2-ResNeXt: A novel model for ECG signal classification
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Date
2023
Abstract
Electrocardiograms (ECG) are the primary basis for the diagnosis of cardiovascular diseases. However, due to the large volume of patients’ ECG data, manual diagnosis is time-consuming and laborious. Therefore, intelligent automatic ECG signal classification is an important technique for overcoming the shortage of medical resources. This paper proposes a novel model for inter-patient heartbeat classification, named G2-ResNeXt, which adds a two-fold grouping convolution (G2) to the original ResNeXt structure, as to achieve better automatic feature extraction and classification of ECG signals. Experiments, conducted on the MIT-BIH arrhythmia database, confirm that the proposed model outperforms all state-of-the-art models considered (except the GRNN model for one of the heartbeat classes), by achieving overall accuracy of 96.16%, and sensitivity and precision of 97.09% and 95.90%, respectively, for the ventricular ectopic heartbeats (VEB), and of 80.59% and 82.26%, respectively, for the supraventricular ectopic heartbeats (SVEB).
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Publisher
IEEE Computer Society
Citation
IEEE Access, 2023, vol. 11, pp. 34808-34820
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Ganchev_2023_G2.pdf
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Funding Information
This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFE0135700; in part by the Tsinghua Precision Medicine Foundation under Grant 2022TS003; in part by the Bulgarian National Science Fund (BNSF) under Grant No. /1 ( P-06-IP-CHINA/1); and in part by the Telecommunications Research Centre (TRC), University of Limerick, Ireland