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Open AccessEditor’s ChoiceArticle

Automatic ECG Diagnosis Using Convolutional Neural Network

Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy
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Electronics 2020, 9(6), 951; https://doi.org/10.3390/electronics9060951
Received: 24 April 2020 / Revised: 1 June 2020 / Accepted: 5 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases. View Full-Text
Keywords: ECG signal detection; cardiovascular diseases; convolutional neural network (CNN); myocardial infarction (MI) ECG signal detection; cardiovascular diseases; convolutional neural network (CNN); myocardial infarction (MI)
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Avanzato, R.; Beritelli, F. Automatic ECG Diagnosis Using Convolutional Neural Network. Electronics 2020, 9, 951.

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