Development and performance evaluation of a coronary artery disease prediction system with transfer learned model based on single lead and multi-lead ECG & TMT-ECG signals

Abstract

Coronary artery disease is one of the major cardiovascular diseases and is a cardiac condition where plaque formed in arteries leads to death worldwide. The identification of CAD in the traditional approach needs a report of ECG, TMT ECG, Pharmacological test, and echocardiogram. The confirmation of CAD leads to undergoing cardiac catheterization. An effective prediction system that can detect the existence of CAD with an initial test like ECG or TMT ECG report image will act as a good assistance to doctors and patients undergoing periodic health monitoring. The present study is focused on developing a prediction system for CAD disease based on raw and filtered, single lead and twelve lead ECG signal images. The algorithm results are compared with transfer learning algorithms. The novelty of the work is highlighted by the fact that the prediction accuracy of the developed algorithm, with single lead and twelve lead ECG or TMT ECG signals (accuracy of approximately 93.5% for single lead and 94.5% for twelve lead) is much higher compared to transfer learned algorithms. The developed model exhibited better accuracy with lesser number of layers compared to deeper pre-trained algorithms.