D-Resnet: Deep Resnet based approach for ECG classification

Sahar Boulkaboul, Samira Bouchama, Syfax Kasser, Belkacem Ait Si Ali


The ECG signal represents the electrical activity of the heart and reflects the health of the cardiovascular system. It also contains information that can be used to differentiate cardiovascular diseases. The automatic classification of arrhythmias is an important step in the development of monitoring equipment in the ambulatory or intensive care setting. In this work we propose a deep residual network (D-Resnet) model which allows a very deep extraction of the characteristics of the ECG signal in order to make the difference between normal and abnormal signals accurately. In the framework of an embedded system project for elderly automated diagnosis, we propose an approach that is designed to classify six types of cardiac rhythms: normal beats, ventricular premature beats, rhythmic beats, atrial premature beats, fusion of ventricular and normal beats or noise. The characteristics and depth of the proposed model makes it possible to provide satisfactory precision in comparison with the work of the literature.

DOI: 10.61416/ceai.v26i1.8816


Deep Learning; ResNet; Electrocardiogram ECG; Arrhythmia classification

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