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Robust heart rate variability analysis by generalized entropy minimization

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  • La Vecchia, Davide
  • Camponovo, Lorenzo
  • Ferrari, Davide

Abstract

Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion function based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital data, an important benchmark data set in HRV analysis.

Suggested Citation

  • La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:137-151
    DOI: 10.1016/j.csda.2014.09.001
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    References listed on IDEAS

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    1. Drost, F.C. & Klaassen, C.A.J. & Werker, B.J.M., 1994. "Adaptive estimation in time-series models," Discussion Paper 1994-88, Tilburg University, Center for Economic Research.
    2. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    3. Mancini, Loriano & Ronchetti, Elvezio & Trojani, Fabio, 2005. "Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 628-641, June.
    4. Machado, José A.F., 1993. "Robust Model Selection and M-Estimation," Econometric Theory, Cambridge University Press, vol. 9(3), pages 478-493, June.
    5. Ronchetti, Elvezio & Trojani, Fabio, 2001. "Robust inference with GMM estimators," Journal of Econometrics, Elsevier, vol. 101(1), pages 37-69, March.
    6. La Vecchia, Davide & Trojani, Fabio, 2010. "Infinitesimal Robustness for Diffusions," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 703-712.
    7. Matías Salibián-Barrera & Stefan Aelst & Gert Willems, 2008. "Fast and robust bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 41-71, February.
    8. Spangl, B. & Dutter, R., 2012. "Analyzing short-term measurements of heart rate variability in the frequency domain using robustly estimated spectral density functions," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1188-1199.
    9. Maharaj, Elizabeth Ann & Alonso, Andrés M., 2014. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 67-87.
    10. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    11. Li, Ta-Hsin, 2008. "Laplace Periodogram for Time Series Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 757-768, June.
    12. Davide Ferrari & Davide La Vecchia, 2012. "On robust estimation via pseudo-additive information," Biometrika, Biometrika Trust, vol. 99(1), pages 238-244.
    13. P. Shi & C‐L. Tsai, 1998. "A note on the unification of the Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 551-558.
    14. Salibian-Barrera, Matias & Van Aelst, Stefan, 2008. "Robust model selection using fast and robust bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5121-5135, August.
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