A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals

Juan P. Amezquita-Sanchez, Nadia Mammone, Francesco C. Morabito, Silvia Marino, Hojjat Adeli

Research output: Contribution to journalArticlepeer-review

Abstract

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%.

Original languageEnglish
Pages (from-to)88-95
Number of pages8
JournalJournal of Neuroscience Methods
Volume322
DOIs
Publication statusPublished - Jul 1 2019

Keywords

  • Alzheimer's disease
  • Electroencephalography
  • EPNN
  • Fractal dimension
  • Hurst exponent
  • Mild cognitive impairment
  • MUSIC-EWT

ASJC Scopus subject areas

  • Neuroscience(all)

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