Oropharyngeal dysphagia identification using wavelets and optimum path forest
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Data
2008-09-05
Autores
Spadotto, André Augusto
Pereira, José Carlos
Guido, Rodrigo Capobianco
Papa, João Paulo
Falcão, Alexandre Xavier
Gatto, Ana Rita [UNESP]
Cola, Paula Cristina [UNESP]
Schelp, Arthur Oscar [UNESP]
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Resumo
The 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.
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International symposium, Pattern classifiers, Acoustic generators, Classification (of information), Diagnosis, Discrete wavelet transforms, Feature extraction, Identification (control systems), Military engineering, Signal processing, Support vector machines, VLSI circuits, Wavelet transforms, Biological organs
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2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008, p. 735-740.