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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases and it is strongly associated with age. There are four AD stages: mild cognitive impairment (MCI), mild, moderate (ADM) and advanced (ADA). 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 power spectrum wavelet packet transform have been extracted from the electroencephalogram signals in order to feed four classifiers: random forest decision trees, linear and quadratic support-vector-machines and linear discriminant analysis. The obtained results were analyzed 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). The applied method was able to detect major differences in scalp areas above the frontal and temporal lobes of the brain as AD progresses.
Original languageEnglish
Pages (from-to)289-302
Number of pages14
JournalInternational Journal of Biomedical Engineering and Technology
Issue number3
Publication statusPublished - 5 Oct 2022


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


Dive into the research topics of 'EEG wavelet packet power spectrum tool for checking alzheimer's disease progression'. Together they form a unique fingerprint.

Cite this