Authors:
Max Chacón
1
;
Claudio Araya
1
;
Marcela Muñoz
1
and
Ronney B. Panerai
2
Affiliations:
1
Universidad de Santiago de Chile, Chile
;
2
University of Leicester, United Kingdom
Keyword(s):
Support vector machine, Artificial neural networks, Cerebral blood flow autoregulation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
The performance of SVMs and ANNs as identifiers of time systems is compared with the purpose of analyzing the Cerebral blood flow Autoregulation System, one of the main systems in the field of cerebral hemodynamics. The main variables of this system are Arterial Blood Pressure (ABP) variations and changes in End-tidal pCO2 (EtCO2). In this work we show that models that have ABP and EtCO2 as input, trained with the SVM, are superior to ANN models in terms of the fit of an unknown set, and they are also more adequate for measuring the influence of EtCO2 on Cerebral Blood Flow Velocity.