Abstract
Atrial fibrillation (AF) is usually detected by inspection of the electrocardiogram waveform, a task made difficult when the signal is distorted by noise. The RR interval time series is more frequently available and accurate, yet linear and nonlinear time series analyses that detect highly varying and irregular AF are vulnerable to the common finding of frequent ectopy. We hypothesized that different nonlinear measures might capture characteristic features of AF, normal sinus rhythm (NSR), and sinus rhythm (SR) with frequent ectopy in ways that linear measures might not. To test this, we studied 2722 patients with 24 h ECG recordings in the University of Virginia Holter database. We found dynamical phenotypes for the three rhythm classifications. As expected, AF records had the highest variability and entropy, and NSR the lowest. SR with ectopy could be distinguished from AF, which had higher entropy, and from NSR, which had different fractal scaling, measured as higher detrended fluctuation analysis slope. With these dynamical phenotypes, we developed successful classification strategies, and the nonlinear measures improved on the use of mean and variability alone, even after adjusting for age. Final models using all variables had excellent performance, with positive predictive values for AF, NSR and SR with ectopy as high as 97, 98 and 90%, respectively. Since these classifiers can reliably detect rhythm changes utilizing segments as short as 10 min, we envision their application in noisy settings and in personal monitoring devices where only RR interval time series may be available.
Highlights
The dynamics of the heart beat have been studied for several decades and there is consensus that advanced mathematical analyses that describe its hallmark features, variability and complexity, yield important clinical information
Each 10 min segment was classified as Atrial fibrillation (AF) if the burden of AF was greater than 5%, as sinus rhythm (SR) with ectopy if the burden of premature atrial contraction (PAC) or premature ventricular contraction (PVC) was more than 10%, as normal sinus rhythm (NSR) otherwise
It is noteworthy that the standard deviations (SD) is as high for the records of SR with ectopy as it is for the AF record, more than twice the variability of NSR
Summary
The dynamics of the heart beat have been studied for several decades and there is consensus that advanced mathematical analyses that describe its hallmark features, variability and complexity, yield important clinical information. There have been successful methods developed for atrial fibrillation (AF) detection based on Poincaré plots (Sarkar et al 2008) and cumulative distribution functions (Tateno and Glass 2001), as well as linear measures. These approaches might be confused, though, by sinus rhythm (SR) with frequent ectopy, for here the overall variability might rise to the level of atrial fibrillation. Since AF, SR with frequent ectopy, and NSR account for the majority of non-paced stable cardiac rhythms, methods in this area are of widespread importance
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