Monitoring the complexity of ventricular response in atrial fibrillation

Atrial fibrillation does not present a uniform extent of variability of the ventricular response exemplifying periodicities and more complex fluctuations, due to varying number and shape of atrial wavelets and aberrant conduction in the AV-junction. It was sought to categorise different degrees of complexity introducing an uncomplicated monitoring method for that objective. The fluctuation of RR-intervals was investigated in 66 patients presenting with atrial fibrillation at different times of the day using conventional statistical methods as well as methods derived from non-linear dynamics. Specifically Poincaré-plots were employed to analyse the data. One hundred and seventy data sets consisting of 5000–8000 intervals each were considered. Statistical methods were shown to describe the observed dynamics not adequately due to background noise in the acquired data as well as owing to intrinsic qualities of the data sets. Namely non-uniformly distributed data and trends within the data sets constituted limitations of statistical methods. Poincaré-plots were proven to be an inexpensive and effective method to categorise the complexity of the ventricular response in atrial fibrillation. Periodical variation of interval lengths could be clearly differentiated from continuous variation in the considered data sets. The latter could be shown to exemplify either stochastic fluctuation or complex non-linear dynamics of RR-interval variation. Thus, different degrees of complexity could be clearly distinguished. It could be shown exemplary that the observed dynamics remained nearly constant within the observation period. Poincaré-plots, thus, provide a means to appreciate the fluctuation of RR-intervals semiquantitatively and to distinguish different patterns of fluctuation dynamics of the ventricular response in atrial fibrillation without being affected by contaminated or inconsistent data. For a complete visualisation of the concerned dynamics relatively small data sets suffice. Thus, it is possible to observe the pattern of fluctuation essentially in real time which makes them ideally suited for future investigation.