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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overall accuracy</i> of 96.16%, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sensitivity</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> 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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