Automatic ECG Diagnosis Using Convolutional Neural Network

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 devel...

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Vydáno v:Electronics (Basel) Ročník 9; číslo 6; s. 951
Hlavní autoři: Avanzato, Roberta, Beritelli, Francesco
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2020
Témata:
Artificial neural networks
Cardiovascular disease
Diagnosis
Echocardiography
Electrocardiography
Heart attacks
Heart diseases
Machine learning
Neural networks
Segments
ISSN:2079-9292, 2079-9292
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Shrnutí: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.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9060951