A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms

Clin Neurol Neurosurg. 2021 Feb:201:106446. doi: 10.1016/j.clineuro.2020.106446. Epub 2020 Dec 29.

Abstract

A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.

Keywords: Alzheimer’s disease; Discrete wavelet transform; Electroencephalograms; Fuzzy logic; Mild cognitive impairment.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Alzheimer Disease / diagnosis*
  • Cognitive Dysfunction / diagnosis*
  • Dementia / classification*
  • Electroencephalography / methods*
  • Entropy
  • Female
  • Fuzzy Logic
  • Humans
  • Male
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted