J Appl Biomed 20:70-79, 2022 | DOI: 10.32725/jab.2022.008

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

Rahul Kumar, Yogender Aggarwal *, Vinod Kumar Nigam
Birla Institute of Technology, Department of Bioengineering and Biotechnology, Mesra, Ranchi, Jharkhand, India

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.

Keywords: Artificial neural network; Atherosclerosis; Coronary artery disease; Heart rate variability; Myocardial infarction; Support vector machine
Conflicts of interest:

The authors have no conflict of interests to declare.

Received: July 20, 2021; Revised: May 10, 2022; Accepted: June 16, 2022; Published: June 21, 2022  Show citation

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Kumar R, Aggarwal Y, Kumar Nigam V. Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine. J Appl Biomed. 2022;20(2):70-79. doi: 10.32725/jab.2022.008. PubMed PMID: 35727124.
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