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

Katerina D Tzimourta,Panagiotis Ioannidis,Alexandros T Tzallas,Nikolaos Grigoriadis,Pantelis Angelidis,Nikolaos Giannakeas,Markos G Tsipouras,Theodora Afrantou,Dimitrios G Tsalikakis,Loukas G Astrakas

doi:10.3390/brainsci9040081

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.

Highlights

Alzheimer’s Disease (AD) is neurogenerative disease of unknown etiology with a great prevalence in western countries [1]
The third problem is a 2-class problem between the controls and mild AD patients (CN/mild), whereas the forth problem consists of EEG features of the controls and moderate AD patients (CN/moderate)
The sixth problem corresponds to the classification among mild and moderate AD patients

Summary

Introduction

Alzheimer’s Disease (AD) is neurogenerative disease of unknown etiology with a great prevalence in western countries [1]. In a recent Alzheimer’s report of 2018 [3], the worldwide AD prevalence was about 33 million patients out of 50 million people suffering from dementia, making AD the most common type of dementia. A variety of diagnostics procedures are performed to evaluate the cognitive and neuropsychological state of patients with dementia, including neuronal and physical examination, brain imaging, and electroencephalographic (EEG) recording. Brain Sci. 2019, 9, 81; doi:10.3390/brainsci9040081 www.mdpi.com/journal/brainsci. Clinical Dementia Rating (CDR) [5] score are a 30-point scale and a 5-point scale respectively, which are utilized by neurologists to evaluate the cognitive decline and functional performance of patients with AD. Higher values of the CDR score indicate a more severe condition, whereas higher values of the MMSE score shows very mild dementia and a healthy condition (MMSE above 28)

Methods

Results

Discussion

Conclusion