Prediction of recurrence of paroxysmal atrial fibrillation based on RR interval

Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence. Copyright © 2019 by Editorial Office of Journal of Biomedical Engineering.