Prediction of atrial fibrillation recurrence after cardioversion—Interaction analysis of cardiac autonomic regulation

Abstract Today atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice accounting for approximately one third of hospitalizations and accompanied with a 5 fold increased risk for ischemic stroke and a 1.5 fold increased mortality risk. The role of the cardiac regulation system in AF recurrence after electrical cardioversion (CV) is still unclear. The aim of this study was to investigate the autonomic regulation by analyzing the interaction between heart rate and blood pressure using novel methods of nonlinear interaction dynamics, namely joint symbolic dynamics (JSD) and segmented Poincare plot analysis (SPPA). For the first time, we applied SPPA to analyze the interaction between two time series. Introducing a parameter set of two indices, one derived from JSD and one from SPPA, the linear discriminant function analysis revealed an overall accuracy of 89% (sensitivity 91.7%, specificity 86.7%) for the classification between patients with stable sinus rhythm (group SR, n  = 15) and with AF recurrence (group REZ, n  = 12). This study proves that the assessment of the autonomic regulation by analyzing the coupling of heart rate and systolic blood pressure provides a potential tool for the prediction of AF recurrence after CV and could aid in the adjustment of therapeutic options for patients with AF.