Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47404
Title: Classification of stationary signals with mixed spectrum
Authors: Saavedra Santana, Pedro 
Santana-Del-Pino, Angelo 
Hernández-Flores, Carmen N. 
Artiles-Romero, Juan 
González-Henríquez, Juan J. 
UNESCO Clasification: 120903 Análisis de datos
240401 Bioestadística
Keywords: Classification
Logit Boost
Mixed spectrum
Stationary processes
Issue Date: 2011
Journal: International Journal Of Biostatistics 
Abstract: This paper deals with the problem of discrimination between two sets of complex signals generated by stationary processes with both random effects and mixed spectral distributions. The presence of outlier signals and their influence on the classification process is also considered. As an initial input, a feature vector obtained from estimations of the spectral distribution is proposed and used with two different learning machines, namely a single artificial neural network and the LogitBoost classifier. Performance of both methods is evaluated on five simulation studies as well as on a set of actual data of electroencephalogram (EEG) records obtained from both normal subjects and others having experienced epileptic seizures. Of the different classification methods, Logitboost is shown to be more robust to the presence of outlier signals
URI: http://hdl.handle.net/10553/47404
ISSN: 1557-4679
DOI: 10.2202/1557-4679.1288
Source: International Journal of Biostatistics [ISSN 1557-4679], v. 7 (1), p. 1-17
Appears in Collections:Artículos
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