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Advisor(s)
Abstract(s)
Alzheimer’s disease (AD) is currently an incurable illness that causes dementia and patient’s condition is progressively worse and
it represents one of the greatest public health challenges worldwide. The main objective of this work was to develop a classification
methodology for EEG signals to improve discrimination amongst patients at varying stages of the illness, Mild Cognitive
Impairment (MCI) patients and non-patients either in order to obtain a more reliable methodology to identify AD in early stages.
For this purpose, a surrogate decision tree classifier was used with 2 different ways of cross-validation (leave-one-out-crossvalidation
and 10-fold-cross validation). The EEG studied features were the values of maxima (NMax) and minima (NMin), the
zero-crossing (Zcr) rate, the mean derivative value at a point (Mdif), the Relative Power (RP) in each of the conventional bands
and finally the spectral ratios (r). The best classification was obtained with vectors of 10 features as classifier entries in a leaveone-
out-cross validation process, reaching 0.934 AUC, a sensitivity of 86.19%, a specificity of 99.35%, an accuracy of 94.88%,
with a low out-of-sample classification error of 6.98% which indicates that the classifier generalizes fairly well.
Description
Keywords
Alzheimer’s diasease Early stages Electroencephalogram signal Surrogate decision tree classifier Features
Citation
Rodrigues, Pedro Miguel; Teixeira, João Paulo; Garrett, Carolina; Alves, Dílio; Freitas, Diamantino (2016). Alzheimer's Early Prediction with Electroencephalogram. In International Conference on Enterprise Information Systems/International Conference on Project Management/International Conference on Health and Social Care Information Systems and Technologies, Centeris/Projman - HCIST 2016. Porto: Elsevier. p. 865-871. ISSN 1877-0509
Publisher
Elsevier