Nonlinear Modeling of Dynamic Cerebral Autoregulation Using Recurrent Neural Networks

  • Max Chacón
  • Cristopher Blanco
  • Ronney Panerai
  • David Evans
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

The function of the Cerebral Blood Flow Autoregulation (CBFA) system is to maintain a relatively constant flow of blood to the brain, in spite of changes in arterial blood pressure. A model that characterizes this system is of great use in understanding cerebral hemodynamics and would provide a pattern for evaluating different cerebrovascular diseases and complications. This work posits a non-linear model of the CBFA system through the evaluation of various types of neural networks that have been used in the field of systems identification. Four different architectures, combined with four learning methods were evaluated. The results were compared with the linear model that has often been used as a standard reference. The results show that the best results are obtained with the FeedForward Time Delay neural network, using the Levenberg-Marquardt learning algorithm, with an improvement of 24% over the linear model (p<0.05).

Keywords

Recurrent Neural Network Cerebral Autoregulation Context Memory Spontaneous Fluctuation Elman Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Max Chacón
    • 1
  • Cristopher Blanco
    • 1
  • Ronney Panerai
    • 2
  • David Evans
    • 2
  1. 1.Informatic Engineering DepartmentUniversity of Santiago de ChileSantiagoChile
  2. 2.Medical Physics Group, Department of Cardiovascular SciencesUniversity of Leicester, Leicester Royal InfirmaryLeicesterUK

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