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Orientador(es)
Resumo(s)
Alzheimer’s Disease (AD) stands out as one of the main causes of dementia
worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD
is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate
AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in
its early stages with the aim of halting the disease progression. Methods: The main purpose of
this study is to develop a system with the ability of differentiate each disease stage by means of
Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet
Packet was performed enabling to extract several features from each study group. Classic Machine
Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG
channel. Results: The maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI),
81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM
vs. ADA) and 56.8% (All vs. All). Conclusions: The proposed method outperforms previous studies
with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain
regions revealed abnormal activity as AD progresses.
Descrição
Palavras-chave
Alzheimer disease Nonlinear multi-band analysis Electroencephalographic signals Classic machine learning Deep learning Wavelet packet Classification
Contexto Educativo
Citação
Araújo, Teresa; Teixeira, João Paulo; Rodrigues, Pedro Miguel (2022). Smart-data-driven system for alzheimer disease detection through electroencephalographic signals. Bioengineering. ISSN 2306-5354. 9:4, p. 1-16