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AI-Based ECG Analysis for Early Detection of Occlusion Myocardial Infarction Using Coronary Angiography Results

Liong-Rung Liu1,2, Hung-Wen Chiu1

  • 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed

Insights

This study developed a deep learning model to detect hidden Occlusion Myocardial Infarction (OMI) in Non-ST Elevation Myocardial Infarction (NSTEMI) patients using ECG data, improving diagnosis and treatment for this high-mortality cardiovascular disease.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Myocardial Infarction (MI) is a leading cause of cardiovascular mortality.
  • ST-Elevation Myocardial Infarction (STEMI) has established treatment protocols.
  • Occlusion Myocardial Infarction (OMI) is often misclassified as Non-ST Elevation Myocardial Infarction (NSTEMI), delaying critical interventions.

Purpose of the Study:

  • To develop and evaluate a deep learning model for identifying OMI from electrocardiogram (ECG) signals.
  • To improve the diagnostic accuracy for OMI cases, particularly within the NSTEMI population.
  • To provide decision support for clinicians in managing acute myocardial infarction.

Main Methods:

  • Collected five years of clinical and 12-lead ECG data from acute myocardial infarction (AMI) patients.
  • Categorized patients into OMI and Non Occlusion Myocardial Infarction (NOMI) groups using angiography and cardiac enzymes.
  • Developed a ResNet-1D deep learning model to analyze ECG signal patterns for OMI detection.

Main Results:

  • The ResNet-1D model achieved an OMI recall of 0.75 and a specificity of 0.53.
  • The model demonstrated the potential to identify OMI cases that might be missed by traditional criteria.
  • Findings highlight the utility of machine learning in augmenting ECG interpretation for OMI.

Conclusions:

  • Machine learning models can effectively assist clinicians in detecting 'hidden' OMI within the NSTEMI population.
  • Deep learning analysis of ECGs offers valuable diagnostic support beyond standard ST-elevation criteria.
  • Improved detection of OMI can lead to timely treatment and better patient outcomes in cardiovascular disease management.

Keywords:
Deep LearningElectrocardiograms (ECG)Occlusion Myocardial Infarction (OMI)

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