Abstract
A model of the Markovian character of the Heart Rate Variability (HRV) is designed by analyzing its information flow. A measure based on higher order cumulants quantifies the dependence of the current value on the past of the time series. That measure is employed as a discriminant statistics to accept or reject the null hypothesis, supposing that a nonlinear Markov process of order n is able to model the given HRV time series. The probability density function characterizing the Markov process is estimated as a sum of Gaussian distributions obtained as outputs of neural networks. The order of the approximating Markov process shows to be a reliable index for quantifying the balance of the autonomic nervous system control activity.
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© 1997 Springer-Verlag Berlin Heidelberg
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Silipo, R., Deco, G., Vergassola, R., Schittenkopf, C., Gremigni, C. (1997). Nonlinear modelling of the daily heart rhythm. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020297
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DOI: https://doi.org/10.1007/BFb0020297
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