Oropharyngeal dysphagia identification using wavelets and optimum path forest

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorSpadotto, André Augusto
dc.creatorPereira, José Carlos
dc.creatorGuido, Rodrigo Capobianco
dc.creatorPapa, João Paulo
dc.creatorFalcão, Alexandre Xavier
dc.creatorGatto, Ana Rita
dc.creatorCola, Paula Cristina
dc.creatorSchelp, Arthur Oscar
dc.date2014-05-27T11:23:39Z
dc.date2016-10-25T18:25:59Z
dc.date2014-05-27T11:23:39Z
dc.date2016-10-25T18:25:59Z
dc.date2008-09-05
dc.date.accessioned2017-04-06T01:32:21Z
dc.date.available2017-04-06T01:32:21Z
dc.identifier2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008, p. 735-740.
dc.identifierhttp://hdl.handle.net/11449/70569
dc.identifierhttp://acervodigital.unesp.br/handle/11449/70569
dc.identifier10.1109/ISCCSP.2008.4537320
dc.identifier2-s2.0-50649108366
dc.identifierhttp://dx.doi.org/10.1109/ISCCSP.2008.4537320
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/891655
dc.descriptionThe swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.
dc.languageeng
dc.relation2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectInternational symposium
dc.subjectPattern classifiers
dc.subjectAcoustic generators
dc.subjectClassification (of information)
dc.subjectDiagnosis
dc.subjectDiscrete wavelet transforms
dc.subjectFeature extraction
dc.subjectIdentification (control systems)
dc.subjectMilitary engineering
dc.subjectSignal processing
dc.subjectSupport vector machines
dc.subjectVLSI circuits
dc.subjectWavelet transforms
dc.subjectBiological organs
dc.titleOropharyngeal dysphagia identification using wavelets and optimum path forest
dc.typeOtro


Este ítem pertenece a la siguiente institución