Descripción
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Voice disorders, which can help in the diagnosis of Parkinson's disease (PD), can be measured with acoustic tools. In this work, demographic data and vocal phonation records /a/ from the available mPower database were analyzed to identify PD patients. A parsimonious model was then found that achieved a reduction from 62 to 5 phonation characteristics, which were considered in addition to gender and age. Multilayer Perceptron (MLP) and Logistic Regression (LR) neural networks were used to obtain a model with high prediction capacity (area under receiver operating characteristic curve, AUC-ROC, over 0.82). This work contributes to the monitoring of EP patients from the recording of a few phonation features collected by means of a mobile phone. | |
Internacional
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Si |
Nombre congreso
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2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) |
Tipo de participación
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960 |
Lugar del congreso
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Bucaramanga, Colombia |
Revisores
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Si |
ISBN o ISSN
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978-1-7281-1491-0 |
DOI
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10.1109/STSIVA.2019.8730219 |
Fecha inicio congreso
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24/04/2019 |
Fecha fin congreso
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26/04/2019 |
Desde la página
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1 |
Hasta la página
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3 |
Título de las actas
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Proceedings of STSIVA ,2019 |