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Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks

Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their a...

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Autores principales: Segovia, Fermín, Illán, Ignacio A., Górriz, Juan M., Ramírez, Javier, Rominger, Axel, Levin, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633498/
https://www.ncbi.nlm.nih.gov/pubmed/26594165
http://dx.doi.org/10.3389/fncom.2015.00137
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author Segovia, Fermín
Illán, Ignacio A.
Górriz, Juan M.
Ramírez, Javier
Rominger, Axel
Levin, Johannes
author_facet Segovia, Fermín
Illán, Ignacio A.
Górriz, Juan M.
Ramírez, Javier
Rominger, Axel
Levin, Johannes
author_sort Segovia, Fermín
collection PubMed
description Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using (18)F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided.
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spelling pubmed-46334982015-11-20 Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks Segovia, Fermín Illán, Ignacio A. Górriz, Juan M. Ramírez, Javier Rominger, Axel Levin, Johannes Front Comput Neurosci Neuroscience Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using (18)F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided. Frontiers Media S.A. 2015-11-05 /pmc/articles/PMC4633498/ /pubmed/26594165 http://dx.doi.org/10.3389/fncom.2015.00137 Text en Copyright © 2015 Segovia, Illán, Górriz, Ramírez, Rominger and Levin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Segovia, Fermín
Illán, Ignacio A.
Górriz, Juan M.
Ramírez, Javier
Rominger, Axel
Levin, Johannes
Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title_full Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title_fullStr Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title_full_unstemmed Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title_short Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
title_sort distinguishing parkinson's disease from atypical parkinsonian syndromes using pet data and a computer system based on support vector machines and bayesian networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633498/
https://www.ncbi.nlm.nih.gov/pubmed/26594165
http://dx.doi.org/10.3389/fncom.2015.00137
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