EEG wavelet packet power spectrum tool for checking alzheimer's disease progression

Pedro Miguel Rodrigues, Cunha Rui Miguel, Gabriel Silva, Marco Alves, Bruno C. Bispo, Dílio Alves, Carolina Garrett

Research output: Contribution to journalArticlepeer-review


Nowadays Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases and it is strongly associated with age. There are four stages of AD: Mild Cognitive Impairment (MCI), Mild, Moderate (ADM) and Advanced (ADA). It has no cure, although there are treatments that can slow down the symptoms. Therefore, a correct diagnose is needed to delay the effects of the disease. This work aims at developing a new tool capable of distinguishing the different stages of AD at scalp level. Features such as the conventional frequencies relative power of the maximum, mean and minimum Power Spectral Density Wavelet Packet Transform (PSDWT) have been extracted from the Electroencephalogram signals (EEG). These features were then selected per electrode to feed four classifiers: Random forest decision trees (CT), linear and quadratic Support-Vector-Machines (SVM) and Linear Discriminant Analysis (LDA).The obtained results were analysed through topographic maps and enabled the distinguish between binary groups with the following overall accuracies: 85.5% (C-MCI); 88.2% (C-ADM); 91.4% (C-ADA); 89.7% (MCI-ADM); 82.4% (MCI-ADA) and 81.3% (ADM-ADA). It is also important to emphasise that there are zones at scalp level with different activities as the disease progresses (100% of accuracy achieved at least in one channel in binary comparisons). The applied method was able to detect major differences in scalp areas above the frontal and temporal lobes of the brain, with great accuracy (100%), as AD progresses.
Original languageEnglish
JournalInternational Journal of Biomedical Engineering and Technology
Publication statusAccepted/In press - 2020


  • Alzheimer's disease
  • Mild cognitive impairment
  • Power spectral density
  • Wavelet packet transform
  • Electroencephalogram signals
  • Classifiers


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