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Electrocardiography (ECG) is a sophisticated tool for the diagnosis of myocardial infarction (MI). Deep learning approaches can support MI diagnosis based on ECG data. However, due to privacy issues and the sensitive nature of ECG data, the use of deep learning methods to access large amounts of ECG data for training remains challenging. Insufficient data will lead to a decrease in diagnosis performance. In addition, most deep learning approaches are trained with multiple-lead ECG data, thereby limiting the extension to portable single-lead ECG monitoring devices. Investigating automated single-lead ECG interpretation is a continuing concern in the development of mobile and wearable ECG monitoring devices. This paper proposes an automated myocardial infarction detection model — SLC-GAN — that synthesizes single-lead ECG data with high morphological similarity through generative adversarial networks (GANs). It automatically detects MI using convolutional neural networks (CNNs) with real ECG data and synthetic ECGs from the GAN. The empirical results indicate that our new SLC-GAN method performs impressively compared with other MI classification methodologies on single-lead ECG from the PTB Diagnostic ECG Database. The MI classification accuracy of SLC-GAN reaches 99.06% with 5-fold cross-validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]