Why the study?
Cardiovascular disease severity causes ECG shape deformation that hinders R-wave detection for HRV analysis, prompting computer-aided ECG-derived features to predict CAD and MI.
Do machine learning models using ECG-derived heart rate variability features accurately predict coronary artery disease and myocardial infarction?
Population
CAD (N = 30), MI (N = 10), and control (N = 30) subjects
Comparison
ANN vs SVM models using ECG-derived HRV features to predict CAD and MI
Authors
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Supports ML classification of CAD/MI using HRV features; leaves open need for prospective validation before clinical use.
Do machine learning models using ECG-derived heart rate variability features accurately predict coronary artery disease and myocardial infarction?
An artificial neural network model using time-domain heart rate variability features from a single-lead ECG can accurately classify patients with coronary artery disease and myocardial infarction.
Kumar et al. (2022) studied this question.
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