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A Stochastic Nonlinear Autoregressive Algorithm Reflects Nonlinear Dynamics of Heart-Rate Fluctuations

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Abstract

Current methods for detecting nonlinear determinism in a time series require long and stationary data records, as most of them 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. To explicitly account for the stochastic portion of the data we recently developed a method based on a stochastic nonlinear autoregressive (SNAR) algorithm. The method iteratively estimates nonlinear autoregressive models for both the deterministic and stochastic portions of the signal. Subsequently, the Lyapunov exponents (LE) are calculated for the estimated models in order to examine if nonlinear determinism is present in the deterministic portion of the fitted model. 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 noninvasively measured resting sinus-rhythm heart-rate signals obtained from 16 patients. Our analysis revealed that a positive LE was highly 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 88% (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 noninvasive probe for screening high-risk populations for malignant cardiac arrhythmias. © 2002 Biomedical Engineering Society.

PAC2002: 8719Hh, 0545Tp, 8710+e

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Authors and Affiliations

  1. Division of Health Sciences and Technology, Harvard University–Massachusetts Institute of Technology, Cambridge, MA

    Antonis A. Armoundas, Nikhil Iyengar & Richard J. Cohen

  2. Dept. of Electrical Engineering, City College of the City University of New York, New York, NY

    Kihwan Ju & Ki H. Chon

  3. Department of Medical Physiology, University of Copenhagen, Copenhagen, Denmark

    Jorgen K. Kanters

  4. Department of Internal Medicine, CCU, Elsinore Hospital, Elsinore, Denmark

    Jorgen K. Kanters

  5. Department of Pediatric Cardiology, Medical University of South Carolina, Charleston, SC

    Philip J. Saul

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  1. Antonis A. Armoundas
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Armoundas, A.A., Ju, K., Iyengar, N. et al. A Stochastic Nonlinear Autoregressive Algorithm Reflects Nonlinear Dynamics of Heart-Rate Fluctuations. Annals of Biomedical Engineering 30, 192–201 (2002). https://doi.org/10.1114/1.1451074

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