Empirical Survey on Occult Atrial Fibrillation Prediction During Sinus Rhythm through Heart Rate Variability Analysis and Premature Atrial Contractions

The prevalence of atrial fibrillation (AF) has significantly increased over the past five decades, establishing it as the leading sustained heart arrhythmia, globally.An elevated risk of cardiovascular disease was observed in patients with AF, particularly in cases of silent paroxysmal AF (PAF).Previous research had relied on ECG monitoring, heart rate variability (HRV), and premature atrial contractions (PAC) assessment to predict the risk of PAF.This study was conducted to replicate and evaluate these methods using the publicly available Icentia-11k Dataset.The data preprocessing was performed following a typical machine learning (ML) pipeline, which included noise filtering and baseline wander removal, prior to feature extraction from HRV in time, frequency, and complexity domains.Various ML classifiers were trained and tested with different sets of features, revealing that the information provided by premature beats, such as PACs, notably influenced specificity at the expense of sensitivity.