Graph theory for feature extraction and classification: A migraine pathology case study

  • Jorge-Hernandez F
  • Chimeno Y
  • Garcia-Zapirain B
 et al. 
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Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.

Author-supplied keywords

  • Functional MRI (fMRI)
  • Graph theory
  • Machine learning
  • Migraine
  • Synchronization likelihood

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  • Fernando Jorge-Hernandez

  • Yolanda Garcia Chimeno

  • Begonya Garcia-Zapirain

  • Alberto Cabrera Zubizarreta

  • Maria Angeles Gomez Beldarrain

  • Begonya Fernandez-Ruanova

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