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Nonlinear modelling of the daily heart rhythm

  • Part VII: Prediction, Forecasting, and Monitoring
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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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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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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