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Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease

Análisis cepstral y la transformada de Hilbert-Huang para la detección automática de la enfermedad de Parkinson

dc.creatorLópez-Pabón, Felipe O.
dc.creatorArias-Vergara, Tomas
dc.creatorOrozco-Arroyave, Juan R.
dc.date2020-01-30
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1401
dc.identifier10.22430/22565337.1401
dc.descriptionMost patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.en-US
dc.descriptionLa mayoría de las personas con la enfermedad de Parkinson (EP) desarrollan varios déficits del habla, incluyendo sonoridad reducida, alteración de la articulación y prosodia anormal. Este artículo presenta una metodología que permite la clasificación automática de pacientes con EP y sujetos de control sanos (CS). Se considera que la transformada de Hilbert-Huang (THH) y los Coeficientes Cepstrales en las frecuencias de Mel modelan las fonaciones moduladas (cambiando el tono de bajo a alto y de alto a bajo) de las vocales /a/, /i/, y /u/. La THH se utiliza para extraer los dos primeros formantes de las señales de audio, con el objetivo de modelar la estabilidad de la lengua mientras los hablantes producen vocales moduladas. Pruebas estadísticas de Kruskal-Wallis se utilizan para eliminar características redundantes y no relevantes, con el fin de mejorar la precisión de la clasificación. La clasificación automática de sujetos con EP vs. CS se realiza mediante una máquina de soporte vectorial de base radial. De acuerdo con los resultados, el enfoque propuesto permite la discriminación automática de sujetos con EP vs. CS con precisiones de hasta el 75 % para los hombres y 73 % para las mujeres.es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1401/1490
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1401/1606
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1401/1587
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dc.rightsCopyright (c) 2020 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 23 No. 47 (2020); 93-108en-US
dc.sourceTecnoLógicas; Vol. 23 Núm. 47 (2020); 93-108es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectSpeech articulationen-US
dc.subjectClassificationen-US
dc.subjectHilbert-Huangen-US
dc.subjectParkinson’s Diseaseen-US
dc.subjectArticulación del hablaes-ES
dc.subjectclasificaciónes-ES
dc.subjectHilbert-Huanges-ES
dc.subjectenfermedad de Parkinsones-ES
dc.titleCepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Diseaseen-US
dc.titleAnálisis cepstral y la transformada de Hilbert-Huang para la detección automática de la enfermedad de Parkinsones-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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