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.
This study has been supported by FONDECYT (Chile) project N° 1050082.
Chapter PDF
References
Panerai, R.: Assessment of cerebral pressure autoregulation in humans - a review of measurement methods. Physiological Measurement 19, 305–338 (1998)
Newell, D., Aaslid, R., Lam, A., Mayberg, T., Winn, R.: Comparison of flow and velocity during autoregulation testing in humans. Stroke 25, 793–797 (1994)
Panerai, R., Evans, D., Mahony, P., Deverson, S., Hayes, P.: Assessment of thigh cuff technique for measurement of dynamic cerebral autoregulation. Stroke 31, 476–480 (2000)
Panerai, R.B., Dawson, S.L., Eames, P.J., Potter, J.F.: Cerebral blood flow velocity response to induced and spontaneous sudden changes in arterial blood pressure. Am. J. Physiol. 280, H2162–H2174 (2001)
Panerai, R., Kelsall, A., Rennie, J., Evans, D.: Cerebral autoregulation dynamics in premature newborns. Stroke 26, 74–80 (1995)
Panerai, R., Dawson, S., Potter, J.: Linear and nonlinear analysis of human dynamic cerebral autoregulation. Am. J. Physiol. 227, H1089–H1099 (1999)
Panerai, R., White, R., Markus, H., Evans, D.: Grading of cerebral dynamic autoregulation from spontaneous fluctuations in arterial blood pressure. Stroke 29, 2341–2346 (1998)
Panerai, R., Rennie, J., Kelsall, A., Evans, D.: Frequency-domain analysis of cerebral autoregulation from spontaneous fluctuations in arterial blood pressure. Med. Biol. Eng. Comput. 36, 315–322 (1998)
Simpson, D., Panerai, R., Evans, D., Naylor, R.: A Parametric Approach to Measuring Cerebral Blood Flow Autoregulation from Spontaneous Variations in Blood Pressure. Annals of Biomedical Engineering 29, 18–25 (2001)
Tiecks, F., Lam, A., Aaslid, R., Newell, D.: Comparison of static and dynamic cerebral autoregulation measurements. Stroke 26, 1014–1019 (1995)
Mitsis, G., Zhang, G., Levine, B.D., Marmarelis, V.Z.: Modeling of Nonlinear Physiological Systems with fast and Slow Dynamics. II. Application to cerebral Autoregulation. Ann. Biomedical Engineering 30, 555–565 (2002)
Panerai, R., Chacón, M., Pereira, R., Evans, D.: Neural Network Modeling of Dynamic Cerebral Autoregulation: Assessment and Comparison with Established Methods. Med. Eng. & Phys. 26, 43–52 (2004)
Mitsis, G., ZPoulin, M., Robbins, A., Marmarelis, V.Z.: Nonlinear Modeling of the Dynamic Effects of Arterial Pressure and CO2 Variations on Cerebral Blood Flow in Healthy Humans. IEEE Trans. Bio. Eng. 51, 1932–1943 (2004)
Principe, J., Euliano, N., Lefebvre, C.: Neural and Adaptive Systems: Fundamentals Through Simulations. John Wiley & Sons, New York (2000)
Hilera, J., Martínez, V.: Redes Neuronales Artificiales. Fundamentos, modelos y aplicaciones. RA-MA, Madrid (1995)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Elman, J.: Finding Structure in Time. Cog. Sci. 4, 141–166 (1990)
Lin, T., Horne, B., Tiňo, P., Giles, C.: Learning Long-Term Dependencies in NARX Recurrent Neural Networks. IEEE Trans. on Neu. Net. 7, 1329–1338 (1996)
Ljung, L.: System Identification – Theory for the User. Prentice Hall, New Yersey (1987)
He, X., Asada, H.: A New Method for Identifying Orders of Input-Output Models for Nonlinear Dynamics Systems. In: Proc. of the American Control Conf., S F, California (1993)
Hornick, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1997)
Mitchell, T.: Machine Learning. WCB/McGraw-Hill, New York (1997)
Panerai, R.B.: The critical closing pressure of the cerebral circulation. Med. Eng. & Phy. 25, 621–632 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chacón, M., Blanco, C., Panerai, R., Evans, D. (2005). Nonlinear Modeling of Dynamic Cerebral Autoregulation Using Recurrent Neural Networks. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_22
Download citation
DOI: https://doi.org/10.1007/11578079_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
Publish with us
-
Published in cooperation with
http://www.iapr.org/
