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A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals.
Amezquita-Sanchez, Juan P; Mammone, Nadia; Morabito, Francesco C; Marino, Silvia; Adeli, Hojjat.
Afiliação
  • Amezquita-Sanchez JP; Autonomous University of Queretaro (UAQ), Faculty of Engineering, Departments Biomedical and Electromechanical, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P. 76807, San Juan del Río, Qro., Mexico.
  • Mammone N; IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, 98124, Messina, Italy.
  • Morabito FC; Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy.
  • Marino S; IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, 98124, Messina, Italy.
  • Adeli H; Departments of Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43220, USA. Electronic address: adeli.1@osu.edu.
J Neurosci Methods ; 322: 88-95, 2019 07 01.
Article em En | MEDLINE | ID: mdl-31055026
BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Sinais Assistido por Computador / Eletroencefalografia / Doença de Alzheimer / Disfunção Cognitiva Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Revista: J Neurosci Methods Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Holanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Sinais Assistido por Computador / Eletroencefalografia / Doença de Alzheimer / Disfunção Cognitiva Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Revista: J Neurosci Methods Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Holanda