Comprehensive analysis of heart rate variability features for accurate prediction of paroxysmal atrial fibrillation

Objective: Paroxysmal atrial fibrillation (PAF) is the first stage of atrial fibrillation and can escalate to a chronic stage without early intervention. This study aims to explore the predictive potential of heart rate variability (HRV) features for PAF and investigate their time variabilities and correlations to enhance prediction accuracy. Method: We analyzed HRV data extracted from 30-min ECG recordings in the Paroxysmal Atrial Fibrillation Prediction Database (AFPDB). A total of 57 features were extracted from each 5-min segment, including timedomain, frequency-domain, and nonlinear features. Statistical tests, recursive feature elimination, feature inclusion and Bayesian networks were employed for feature optimization, and supervised learning classifiers and 10-fold cross validation were utilized for PAF prediction. Results: The random forest classifier demonstrated the best predictive performance, with accuracy, sensitivity, specificity, and F1 score reaching 98.0%, 96.7%, 100.0% and 0.980, respectively. Time-domain features consistently showed excellent classification abilities. Bispectral features and Poincare ' plot features performed well in specific segments. The analysis of feature interactions provided valuable insights into the relationships among different feature groups. Conclusion: Our comprehensive analysis of HRV features reveals their significance in predicting PAF and emphasizes the importance of considering feature time variabilities and correlations. The proposed prediction method with novel feature optimization strategies shows promising results in enhancing prediction accuracy. Significance: The accurate prediction of PAF using HRV features has important clinical implications for early detection and intervention. The findings contribute to the advancement of AF prediction and personalized healthcare strategies.