Abstract
Since the appearance of methods based on machine learning, they have been presented as an alternative to classical phenomenological modeling and there are few initiatives that attempt to integrate them. This paper presents a hybrid paradigm called gray box that blends a phenomenological description (differential equation) and a Support Vector Machine (SVM) to model a relevant problem in the field of cerebral hemodynamic. The results show that with this type of paradigm it is possible to exceed the results obtained with phenomenological models and also with the models based on learning, in addition to contributing to the description of the modelled phenomenon.
Chapter PDF
Similar content being viewed by others
References
Psichogios, D., Ungar, L.: A Hybrid Neural Networks First Principles Approach to Process Modeling. AIChE J. 38, 1499–1511 (1992)
Thompson, M., Kramer, M.: Modeling Chemical Processes Using Prior Knowledge and Neural Networks. AIChE J. 40, 1328–1340 (1994)
Anderson, J., McAvoy, T., Hao, O.: Use of Hybrid Models in Wastewater Systems. Ind. Eng. Chem. Res. 39, 94–1704 (2000)
Thibault, J., Acuña, G., Pérez-Correa, R., Jorquera, R., Molin, P., Agosin, E.: A hybrid representation approach for modelling complex dynamic bioprocesses. Bioprocess Engineering 22(6), 547–556 (2000)
Acuña, G., Cubillos, F., Thibault, J., Latrille, E.: Comparison of Methods for Training Grey- box Neural Models. Computers. Chem. Engng. 23, S561–S564 (1999)
Panerai, R.B.: Assesment of cerebral pressure autoregulation in humans - a review of measurement methods. Physiological Measurement 9, 305–338 (1998)
Simpson, D., Panerai, R., Evans, D., Garnham, J., Naylor, A., Bell, P.: Estimating normal and pathological flow velocity to step changes in end-tidal PCO2. Medical & Biological Engineering & Computing 38, 535–539 (2000)
Poulin, M., Liang, P., Robbins, P.: Dynamics of the cerebral blood flow response to step changes in end-tidal PCO2 and PO2 in humans. Journal of Applied Physiology 81, 1084–1095 (1996)
Mitsis, G., Poulin, M., Robbins, P., Marmarelis, V.: Nonlinear modeling of the dynamic effects of arterial pressure and CO2 variations on cerebral blood flow in healthy humans. IEEE Transactions on Biomedical Engineering 51, 1932–1943 (2004)
Chacón, M., Araya, C., Panerai, R.B.: Non-linear multivariate modeling of cerebral hemodynamics with autoregressive Support Vector Machines. Medical Engineering & Physics 33(2), 180–187 (2011)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Neural Computation 2, 1083–1121 (1998)
Tiecks, F., Lam, A., Aaslid, R., Newell, D.: Comparison of static and dynamic cerebral autoregulation measurements. Stroke 26, 1014–1019 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chacón, M., Severino, M., Panerai, R. (2011). Gray Box Model with an SVM to Represent the Influence of PaCO2 on the Cerebral Blood Flow Autoregulation. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_75
Download citation
DOI: https://doi.org/10.1007/978-3-642-25085-9_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25084-2
Online ISBN: 978-3-642-25085-9
eBook Packages: Computer ScienceComputer Science (R0)