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EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

1
Department of Medical Physics, Medical School, University of Ioannina, GR45110 Ioannina, Greece
2
Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece
3
2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece
4
Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(4), 81; https://doi.org/10.3390/brainsci9040081
Received: 8 March 2019 / Revised: 10 April 2019 / Accepted: 10 April 2019 / Published: 14 April 2019
(This article belongs to the Collection Collection on Clinical Neuroscience)
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Abstract

Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems. View Full-Text
Keywords: Alzheimer’s Disease; EEG; detection; mild; moderate; dementia; classification; Random Forests; window length Alzheimer’s Disease; EEG; detection; mild; moderate; dementia; classification; Random Forests; window length
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Tzimourta, K.D.; Giannakeas, N.; Tzallas, A.T.; Astrakas, L.G.; Afrantou, T.; Ioannidis, P.; Grigoriadis, N.; Angelidis, P.; Tsalikakis, D.G.; Tsipouras, M.G. EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions. Brain Sci. 2019, 9, 81.

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