Treffer: Support vector machine with external recurrences for modeling dynamic cerebral autoregulation
Title:
Support vector machine with external recurrences for modeling dynamic cerebral autoregulation
Authors:
Source:
Progress in pattern recognition, image analysis and applications (11th Iberoamerican congress in pattern recognition, CIARP 2006, Cancun, Mexico, November 14-17, 2006)0CIARP 2006. :954-963
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 18 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse régression, Regression analysis, Análisis regresión, Analyse statistique, Statistical analysis, Análisis estadístico, Appareil circulatoire, Circulatory system, Aparato circulatorio, Application médicale, Medical application, Aplicación medical, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Autorégulation, Self regulation, Autoregulación, Classification, Clasificación, Débit sanguin, Blood flow, Flujo sanguíneo, Fonction potentiel, Potential function, Función potencial, Hémodynamique, Hemodynamics, Hemodinámica, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Modèle biologique, Biological model, Modelo biológico, Modèle dynamique, Dynamic model, Modelo dinámico, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Récurrence, Recurrence, Recurrencia, Réseau neuronal, Neural network, Red neuronal, Système dynamique, Dynamical system, Sistema dinámico, Système nerveux central, Central nervous system, Sistema nervioso central, Série temporelle, Time series, Serie temporal, Traitement image, Image processing, Procesamiento imagen
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Universidad de Santiago de Chile; Departamento de Ingeniería Informática, Av. Ecuador No 3659 -Casilla 10233, Santiago, Chile
Medical Physics Group, Department of Cardiovascular Sciences, University of Leicester, Leicester Royal Infirmary, Leicester LEI 5WW, United Kingdom
Medical Physics Group, Department of Cardiovascular Sciences, University of Leicester, Leicester Royal Infirmary, Leicester LEI 5WW, United Kingdom
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19079031
Database:
PASCAL Archive
Weitere Informationen
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.