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Published on: June 5, 2019
Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural
Rahul Kumar1, Yogender Aggarwal1, Vinod Kumar Nigam1
1Birla Institute of Technology, Department of Bioengineering and Biotechnology, Mesra, Ranchi, Jharkhand, India.
Insights
Electrocardiogram (ECG) derived heart rate variability (HRV) analysis effectively predicts coronary artery disease (CAD) and myocardial infarction (MI). This non-invasive method shows high accuracy in identifying patients with these atherosclerotic conditions.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Atherosclerosis is a primary driver of coronary artery disease (CAD) and myocardial infarction (MI), leading causes of global mortality.
- Electrocardiogram (ECG) derived heart rate variability (HRV) offers a potential non-invasive method for computer-aided prognosis of atherosclerotic events.
Purpose of the Study:
- To evaluate the efficacy of ECG-derived HRV features in distinguishing between subjects with CAD, MI, and healthy controls.
- To assess the performance of artificial neural networks (ANN) and support vector machines (SVM) in classifying these subject groups based on HRV.
Main Methods:
- Collected lead-II ECG data from 70 male subjects (aged 55 ± 5 years), including CAD, MI, and control groups.
- Extracted 25 HRV features from the ECG signals and analyzed using one-way ANOVA.
- Trained and tested ANN and SVM models using the extracted HRV features for classification.
Main Results:
- Significant differences in HRV were observed between atherosclerotic subjects (CAD, MI) and controls.
- ANN achieved 100% accuracy in classifying CAD/MI from controls; SVM achieved 99.6%.
- SVM and ANN demonstrated high accuracy (99.3% and 99.0%, respectively) in differentiating CAD from MI.
Conclusions:
- Depressed HRV is a potential marker for identifying atherosclerotic events.
- The use of HRV analysis with machine learning models presents a non-invasive, cost-effective approach for the prognosis of CAD and MI.
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.
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