Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

Shrivathsa Thokur Vasudeva, Shrikantha Sasihithlu Rao, Navin Karanth Panambur, Arun Kumar Shettigar, Chakrapani Mahabala, Padmanabh Kamath, Manjunath Patel Gowdru Chandrashekarappa, Emanoil Linul

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%.

Original languageEnglish
Article number7711
JournalApplied Sciences (Switzerland)
Volume12
Issue number15
DOIs
Publication statusPublished - 08-2022

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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