1. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
- Author
-
Muhammad Adam, Shu Lih Oh, Yuki Hagiwara, U. Rajendra Acharya, Jen Hong Tan, and Hamido Fujita
- Subjects
Information Systems and Management ,Computer science ,Feature extraction ,02 engineering and technology ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,Theoretical Computer Science ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,cardiovascular diseases ,Myocardial infarction ,business.industry ,Deep learning ,Pattern recognition ,medicine.disease ,Computer Science Applications ,Noise ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Ecg signal ,business ,Software - Abstract
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.
- Published
- 2017