1. Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework
- Author
-
Houmani, Nesma, Vialatte, François, Gallego-Jutglà, Esteve, Dreyfus, Gérard, Nguyen-Michel, Vi-Huong, Mariani, Jean, Kinugawa, Kiyoka, ARMEDIA (ARMEDIA-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Département Electronique et Physique (EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS), Laboratoire Plasticité du Cerveau Brain Plasticity (UMR 8249) (PdC), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Data and Signal Processing Research Group (U Science Tech - University of Vic), Département Hospitalo-Universitaire Fight Ageing and STress (DHU FAST ), Université Pierre et Marie Curie - Paris 6 (UPMC), Adaptation Biologique et Vieillissement = Biological Adaptation and Ageing (B2A), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ARMEDIA ( ARMEDIA-SAMOVAR ), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux ( SAMOVAR ), Institut Mines-Télécom [Paris]-Télécom SudParis ( TSP ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut Mines-Télécom [Paris]-Télécom SudParis ( TSP ) -Centre National de la Recherche Scientifique ( CNRS ), Département Electronique et Physique ( EPH ), Institut Mines-Télécom [Paris]-Télécom SudParis ( TSP ), Institut Mines-Télécom [Paris]-Télécom SudParis ( TSP ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Plasticité du Cerveau = Brain Plasticity Laboratory - (CNRS UMR 8249 - ESPCI ParisTech), ESPCI ParisTech, Département Hospitalo-Universitaire Fight Ageing and STress ( DHU FAST ), Université Pierre et Marie Curie - Paris 6 ( UPMC ), Adaptation Biologique et Vieillissement = Biological Adaptation and Ageing ( B2A ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Plasticité du Cerveau (PdC), and ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS)-Paris Sciences et Lettres (PSL)
- Subjects
[SCCO.NEUR]Cognitive science/Neuroscience ,[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology ,lcsh:R ,lcsh:Medicine ,[MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT] ,[ MATH.MATH-IT ] Mathematics [math]/Information Theory [math.IT] ,[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,[ SDV.NEU.NB ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology ,[ SCCO.NEUR ] Cognitive science/Neuroscience ,lcsh:Q ,[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST] ,lcsh:Science ,[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR] ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patient
- Published
- 2018
- Full Text
- View/download PDF