Oropharyngeal Dysphagia Identification Using Wavelets And Optimum Path Forest

dc.creatorSpadotto A.A.
dc.creatorPereira J.C.
dc.creatorGuido R.C.
dc.creatorPapa J.P.
dc.creatorFalcao A.X.
dc.creatorGatto A.R.
dc.creatorCola P.C.
dc.creatorSchelp A.O.
dc.date2008
dc.date2015-06-30T19:22:32Z
dc.date2015-11-26T15:32:03Z
dc.date2015-06-30T19:22:32Z
dc.date2015-11-26T15:32:03Z
dc.date.accessioned2018-03-28T22:40:32Z
dc.date.available2018-03-28T22:40:32Z
dc.identifier9781424416882
dc.identifier2008 3rd International Symposium On Communications, Control, And Signal Processing, Isccsp 2008. , v. , n. , p. 735 - 740, 2008.
dc.identifier
dc.identifier10.1109/ISCCSP.2008.4537320
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-50649108366&partnerID=40&md5=111edb38ee340254e9dc52b15cfc34cf
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/105984
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/105984
dc.identifier2-s2.0-50649108366
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1262388
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.description
dc.description
dc.description735
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dc.languageen
dc.publisher
dc.relation2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008
dc.rightsfechado
dc.sourceScopus
dc.titleOropharyngeal Dysphagia Identification Using Wavelets And Optimum Path Forest
dc.typeActas de congresos


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