A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection

Oltu B., Aksahin M. F., Kibaroglu S.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, cilt.63, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 63
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.bspc.2020.102223
  • Dergi Adı: BIOMEDICAL SIGNAL PROCESSING AND CONTROL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, INSPEC
  • Anahtar Kelimeler: Alzheimer's disease, Mild cognitive impairment, EEG, Discrete wavelet transform, Coherence, Power spectral density, Machine learning, EEG COHERENCE, SCALP EEG, CLASSIFICATION, SIGNALS, VALIDATION, COMPLEXITY, DIAGNOSIS, ALGORITHM, DEMENTIA, DYNAMICS
  • Gazi Üniversitesi Adresli: Hayır

Özet

Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals.