Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

•Classification of normal and MI ECG beats.•With and without noise ECG beats are considered.•Convolutional neural network is employed.•R peak detection is not performed.•Accuracy of 93.53% and 95.22% obtained for with and without noise respectively The electrocardiogram (ECG) is a useful diagnostic...

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Vydané v:Information sciences Ročník 415-416; s. 190 - 198
Hlavní autori: Acharya, U. Rajendra, Fujita, Hamido, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adam, Muhammad
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.11.2017
Predmet:
Convolution neural network
Deep learning
Electrocardiogram signals
Myocardial infarction
Deep learning
Myocardial infarction
Electrocardiogram signals
Convolution neural network
ISSN:0020-0255, 1872-6291
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Popis
Shrnutí:•Classification of normal and MI ECG beats.•With and without noise ECG beats are considered.•Convolutional neural network is employed.•R peak detection is not performed.•Accuracy of 93.53% and 95.22% obtained for with and without noise respectively The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2017.06.027