Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine.

Background: Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events.
Methods: A total of 70 male subjects aged 55 ± 5 years participated in the study. The lead-II ECG was recorded and sampled at 200 Hz. The tachogram was obtained from the ECG signal and used to extract twenty-five HRV features. The one-way Analysis of variance (ANOVA) test was performed to find the significant differences between the CAD, MI, and control subjects. Features were used in the training and testing of a two-class artificial neural network (ANN) and support vector machine (SVM).
Results: The obtained results revealed depressed HRV under atherosclerosis. Accuracy of 100% was obtained in classifying CAD and MI subjects from the controls using ANN. Accuracy was 99.6% with SVM, and in the classification of CAD from MI subjects using SVM and ANN, 99.3% and 99.0% accuracy was obtained respectively.
Conclusions: Depressed HRV has been suggested to be a marker in the identification of atherosclerotic events. The good accuracy observed in classification between control, CAD, and MI subjects, revealed it to be a non-invasive cost-effective approach in the prognosis of atherosclerotic events.
Competing Interests: The authors report no conflicts of interest in this work.