Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Journal: Parkinsonism & related disorders
Published Date: Feb 1, 2018

Abstract

BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others.

Authors

  • Rita Morisi
    IMT School for Advanced Studies, Piazza S. Ponziano, 6, 55100, Lucca, Italy.
  • David Neil Manners
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Giorgio Gnecco
    IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.
  • Nico Lanconelli
    Department of Physics and Astronomy, University of Bologna, Viale Berti-Pichat 6/2, 40127, Bologna, Italy.
  • Claudia Testa
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Stefania Evangelisti
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Lia Talozzi
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Laura Ludovica Gramegna
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Claudio Bianchini
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Giovanna Calandra-Buonaura
    Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy; IRCCS Institute of Neurological Sciences of Bologna, Via Altura 3, 40139, Bologna, Italy.
  • Luisa Sambati
    Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Giulia Giannini
    Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Pietro Cortelli
    Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy; IRCCS Institute of Neurological Sciences of Bologna, Via Altura 3, 40139, Bologna, Italy.
  • Caterina Tonon
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy.
  • Raffaele Lodi
    Functional MR Unit, Policlinico S. Orsola - Malpighi, Via Massarenti 9, 40138, Bologna, Italy; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Via U. Foscolo 7, 40123, Bologna, Italy. Electronic address: raffaele.lodi@unibo.it.

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