Support vector machine with external recurrences for modeling dynamic cerebral autoregulation

Authors:
Max Chacón;Darwin Diaz;Luis Ríos;David Evans;Ronney Panerai
Affiliations:
Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Casilla, Santiago, Chile;Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Casilla, Santiago, Chile;Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Casilla, Santiago, Chile;Medical Physics Group, Department of Cardiovascular Sciences, University of Leicester, Leicester Royal Infirmary, Leicester, UK;Medical Physics Group, Department of Cardiovascular Sciences, University of Leicester, Leicester Royal Infirmary, Leicester, UK
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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Abstract

Support Vector Machines (SVM) have been applied extensively to classification and regression problems, but there are few solutions proposed for problems involving time-series. To evaluate their potential, a problem of difficult solution in the field of biological signal modeling has been chosen, namely the characterization of the cerebral blood flow autoregulation system, by means of dynamic models of the pressure-flow relationship. The results show a superiority of the SVMs, with 5% better correlation than the neural network models and 18% better than linear systems. In addition, SVMs produce an index for measuring the quality of the autoregulation system which is more stable than indices obtained with other methods. This has a clear clinical advantage.