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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| Vydáno v: | Information sciences Ročník 415-416; s. 190 - 198 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.11.2017
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| Témata: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line přístup: | Získat plný text |
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Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Oh, Shu Lih Hagiwara, Yuki Acharya, U. Rajendra Tan, Jen Hong Fujita, Hamido Adam, Muhammad |
| Author_xml | – sequence: 1 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 2 givenname: Hamido surname: Fujita fullname: Fujita, Hamido email: HFujita-799@acm.org organization: Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate 020-0693, Japan – sequence: 3 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 4 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 5 givenname: Jen Hong surname: Tan fullname: Tan, Jen Hong organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 6 givenname: Muhammad surname: Adam fullname: Adam, Muhammad organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore |
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