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Neural Computing and Applications

, Volume 21, Issue 8, pp 2065–2070 | Cite as

Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability

  • D. PetkovićEmail author
  • Ž. Ćojbašić
Original Article

Abstract

Heart rate signal can be used as certain indicator of heart disease. Spectral analysis of heart rate variability (HRV) signal makes it possible to partly separate the low-frequency (LF) sympathetic component, from the high-frequency (HF) vagal component of autonomic cardiac control. Here, we used two important features to characterize the nonlinear fluctuations in the heart variability signal (HRV): cardiac vagal index (CVI) and cardiac sympathetic index (CSI) which indicates vagal and sympathetic function separately. This article presents a methodology for analyzing the influence of CVI and CSI on heart rate variability spectral patterns—low-frequency (LF) and high-frequency (HF) spectral bands and LF/HF ratio. An adaptive neuro-fuzzy network is used to approximate correlation between these two features and spectral patterns. This system is capable to find any change in ratio of features and spectral patterns of heart rate variability signal (HRV) and thus indicates state of both parasympathetic and sympathetic functions in newly diagnosed patients with heart diseases.

Keywords

Heart rate Neuro-fuzzy Nervous system Poincare plot Spectral frequency 

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of Niš, Mechanical Engineering FacultyNišSerbia
  2. 2.Department of Mechatronics and ControlUniversity of NišNisSerbia

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