Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Autonomic Nervous System Research

Background:

• High-fat diets (HFD) and lifestyle changes contribute to atherosclerosis, a condition linked to cardiovascular diseases and autonomic dysfunction.
• Autonomic dysfunction, characterized by altered heart rate variability (HRV), is increasingly recognized as a consequence of atherosclerosis.

Purpose of the Study:

• To investigate the correlation between autonomic activity, assessed via HRV, and key lipid and atherosclerosis markers.
• To develop and evaluate a support vector machine (SVM) based model for predicting the severity of atherosclerosis.

Main Methods:

• Weekly measurements of Lead-II electrocardiograms and blood markers over nine weeks in control and experimental groups.
• Derivation of time-domain HRV parameters and correlation analysis with lipid and atherosclerosis markers.
• Utilizing statistically significant HRV parameters as features for an SVM model to predict atherosclerosis severity.

Main Results:

• Progressive atherosclerosis severity, induced by HFD, was associated with reduced time-domain HRV parameters.
• A significant negative correlation was observed between HRV parameters and lipid/atherosclerosis markers.
• The SVM model achieved prediction accuracies ranging from 86.58% to 98.71% for atherosclerosis severity.

Conclusions:

• Atherosclerosis progression is linked to autonomic dysfunction and diminished HRV.
• Autonomic parameters, particularly vagal activity, may play a crucial role in the prognosis of atherosclerosis.
• The developed SVM model demonstrates high accuracy in predicting atherosclerosis severity.