Physiopathological Insights into Atrial Fibrillation Onset through Heart Rate Variability Correlations

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. Early prediction of AF episodes remains a clinical challenge. This study aimed to generate physiopathological hypotheses for AF onset by analyzing correlations among heart rate variability (HRV) parameters in patients monitored via long-term Holter ECG. We utilized the IRIDIA-AF database, comprising 1319 paroxysmal AF episodes from 872 patients. An XGBoost machine learning model was developed to predict AF onset within 24 h using short- and long-term HRV features, fragmentation indices, and non-linear metrics extracted during sinus rhythm. Model interpretation was performed using SHapley Additive exPlanations (SHAP) values, and dimensionality reduction techniques were applied for data visualization. The model achieved an area under the receiver operating characteristic curve of 0.919 and an area under the precision-recall curve of 0.919, with high accuracy, sensitivity, and specificity. Key predictive features included short-term vagal activity, HRV fragmentation indices, and non-linear parameters, highlighting the role of the autonomic nervous system in AF initiation. Our findings suggest that distinct physiological profiles, detectable via HRV, may underlie AF susceptibility and could inform personalized monitoring and prevention strategies.