Analysis of EEG sleep spindle parameters from apnea patients using massive computing and decision tree

dc.contributorUniversidade de Caxias do Sul (UCS)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade Federal do Rio Grande do Sul (UFRGS)
dc.contributorUniversidade Federal de Sergipe (UFS)
dc.date.accessioned2016-07-07T12:35:32Z
dc.date.available2016-07-07T12:35:32Z
dc.date.created2016-07-07T12:35:32Z
dc.date.issued2014
dc.identifierScientia Cum Industria, v. 2, n. 1, p. 15-18, 2014.
dc.identifier2318-5279
dc.identifierhttp://hdl.handle.net/11449/140812
dc.identifier10.18226/23185279.v2iss1p15
dc.identifierISSN2318-5279-2014-02-01-15-18.pdf
dc.identifier7977035910952141
dc.description.abstractIn this study, Matching Pursuit (MP) procedure is applied to the detection and analysis of EEG sleep spindles in patients evaluated for suspected OSAS. Elements having the frequency of EEG sleep spindles are selected from different dictionary sizes, with and without a frequency modulation function (chirp) for signal description. This procedure was done with high computational cost in order to find best parameters for real EEG data description. At the end we used the atom parameters as input for a decision tree-based classifier, making possible to obtain a classification according to apnea-hypopnea index group and allowing to see how atom parameters such as frequency and amplitude are affected by the presence of sleep apnea.
dc.languageeng
dc.relationScientia Cum Industria
dc.rightsAcesso aberto
dc.sourceCurrículo Lattes
dc.subjectEEG
dc.subjectSignal analysis
dc.subjectMatching pursuit
dc.subjectObstructive apnea
dc.subjectMachine learning
dc.subjectDecision tree
dc.titleAnalysis of EEG sleep spindle parameters from apnea patients using massive computing and decision tree
dc.typeArtículos de revistas


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