Autonomic Features in Prediction of Coronary Artery Disease and Myocardial Infarction

Atherosclerosis is one of the causes of the progression of cardiovascular diseases that lead to coronary artery diseases (CAD) and myocardial infarction (MI). The disease severity causes electrocardiogram (ECG) shape deformation leads to difficulty in R-wave detection required for heart rate variability (HRV) analysis. This limitation in the diagnosis of atherosclerotic events using ECG leads to computer-aided ECG-derived features in the prediction of CAD and MI. In this study, the lead-II ECG was recorded from CAD (N = 30), MI (N = 10), and control (N = 30) subjects. The ECG-derived heart rate variability (HRV) features were extracted. The HRV features-based support vector machine (SVM) and the artificial neural network (ANN) models have been optimized in the prediction of CAD and MI subjects. The present study revealed an efficacy of time-domain HRV parameters with the ANN model in the classification of CAD and MI subjects with an accuracy of 100% and 99.6%, respectively. While SVM presented an accuracy of 75.5% and 98.9%, respectively. The time-domain HRV parameter-based automated computer-aided diagnostic approach can be used in developing low-cost technology that may provide aid to clinicians in the screening of CAD and MI subjects.