SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis

Wenqiang Li, Yuk Ming Tang, Kai Ming Yu, Suet To

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)738-750
Number of pages13
JournalInformation Sciences
Publication statusPublished - Apr 2022


  • Convolutional neural networks
  • ECG
  • Generative adversarial networks
  • Myocardial infarction
  • Single lead

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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