Prediction of the outcome of electrophysiologic study using nonlinear dynamic analysis of heart rate fluctuations

It has been reported that nonlinear deterministic methods can be used as diagnostic and prognostic tools to determine the state of health of cardiac arrhythmia. The accuracy of the methods and consequently the application to cardiac arrhythmia is in doubt because the current methods for detecting deterministic chaos in a time series require long and stationary data records. In addition, most of these methods assume that the observed dynamics arise only from the internal, deterministic workings of the system, and the stochastic portion of the signal (the noise component) is assumed to be negligible. As a consequence, many of the existing methods for detecting and quantifying chaotic dynamics will give erroneous results when applied to actual clinical data. To explicitly account for the stochastic part of the data we have recently developed a method based on the Stochastic Nonlinear Autoregressive (SNAR) algorithm (9). The method iteratively estimates NAR models for both the deterministic and stochastic portions of the signal. Subsequently, Lyapunov exponents (LE) are calculated for the estimated models to examine if chaotic determinism (i.e., sensitivity to initial conditions) is present in the time series. Due to specific modeling of the stochastic portion of the contaminated signal, the SNAR algorithm has been shown to be more accurate than most other methods in detecting nonlinear dynamics even in conditions of short and noisy time series. We show several computer simulation examples to demonstrate the efficacy of the SNAR method. To determine if nonlinear dynamic analysis of heart rate fluctuations can be used to assess arrhythmia susceptibility by predicting the outcome of invasive cardiac electrophysiologic study (EPS), we applied the SNAR algorithm to non-invasively measured resting sinus-rhythm heart-rate signals obtained from 16 patients. Our analysis revealed that a positive LE was correlated to a patient with a positive outcome of EPS. We found that the statistical accuracy of the SNAR algorithm in predicting the outcome of EPS was 83% (sensitivity=100%, specificity=75%, positive predictive value=80%, negative predictive value=100%, p=0.0019). Our results suggest that the SNAR algorithm may serve as a non-invasive probe for screening high-risk populations for malignant cardiac arrhythmias.