Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

Journal: Journal of Alzheimer's disease : JAD
Published Date: Jan 1, 2020

Abstract

BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests.

Authors

  • Fabrizio Vecchio
    Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.
  • Francesca Miraglia
    Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.
  • Francesca Alù
    Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
  • Matteo Menna
    Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
  • Elda Judica
    Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy.
  • Maria Cotelli
    Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy.
  • Paolo Maria Rossini
    Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.

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