title: Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks.
abstract: 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.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Fermín
authors.initials: F
authors.lastname: Segovia
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Ignacio A
authors.initials: IA
authors.lastname: Illán
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Juan M
authors.initials: JM
authors.lastname: Górriz
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Ramírez
authors.affiliation: Department of Nuclear Medicine, Ludwig Maximilian University of Munich Munich, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Rominger
authors.affiliation: Department of Neurology, University of Munich Munich, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Levin
keywords: 18F-DMFP PET; Bayesian network; Parkinson's disease; multivariate analysis; support vector machine