Key result
Nonlinear pulse rate variability features from PPG using a support vector machine achieved 98.2% and 98.3% accuracy in predicting CHD and MCI subjects from controls, respectively.
Why the study?
Atherosclerosis may induce coronary heart diseases that progress to myocardial infarction if untreated, prompting the need for a noninvasive, low-cost, wearable technology to forecast these conditions using PPG-derived features with machine learning.
Does a support vector machine using PPG-derived nonlinear pulse rate variability features accurately identify subjects with coronary heart disease and myocardial infarction?
Cross-Sectional (n=70)
Does a support vector machine using PPG-derived nonlinear pulse rate variability features accurately identify subjects with coronary heart disease and myocardial infarction?
Answers are grounded in this paper and cite their sources.
Nonlinear pulse rate variability features derived from a digital photoplethysmogram can accurately classify patients with coronary heart disease and myocardial infarction from healthy controls using a support vector machine.
Atherosclerosis may induce coronary heart diseases (CHD) that progress to myocardial infarction (MCI), if untreated. The pulse plethysmogram (PPG) plot demonstrated the change in peripheral blood volume influence induced by the sympathetic branch of the autonomic nervous system that causes vasoconstriction. Thus, the present work has been aimed at proposing a noninvasive, low-cost, and wearable technology for the forecasting of CHD and MCI subjects using PPG-derived features with a support vector machine (SVM). A total of 70 volunteers (50-55 years) including MCI (n = 10), CHD (n = 30), and Control (n = 30) has been participated. Digital PPG was recorded for 10 min from the index finger of the left hand in the supine position. Ten samples from each subject were selected from the PPG signal for deriving nonlinear pulse rate variability (PRV) features using Kubios 2.0. The sensitivity, specificity, and accuracy were calculated from the obtained confusion matrix in the classification of control, CHD, and MCI classes. The findings suggested that the reduced value of PRV features in the Poincare plot, detrended fluctuation analysis recurrence plot, and correlation dimension while higher entropy measures in MCI than the CHD subjects. The best accuracy of 98.2% and 98.3% were observed with quadratic functions in the prediction of CHD and MCI subjects from the control group, respectively. The obtained results suggested the applicability of nonlinear PRV features in designing the low-cost real-time wearable technique in the prediction of CHD and MCI subjects.
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Kumar et al. (2023) conducted a cross-sectional in Coronary Heart Disease and Myocardial Infarction (n=70). Nonlinear pulse rate variability (PRV) features from PPG vs. Control group was evaluated on Classification accuracy of CHD and MCI subjects. Nonlinear pulse rate variability features from PPG using a support vector machine achieved 98.2% and 98.3% accuracy in predicting CHD and MCI subjects from controls, respectively.
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