EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions
Random Forests
moderate
detection
window length
Neurosciences. Biological psychiatry. Neuropsychiatry
Article
3. Good health
03 medical and health sciences
0302 clinical medicine
classification
mild
Alzheimer’s Disease
EEG
dementia
RC321-571
DOI:
10.3390/brainsci9040081
Publication Date:
2019-04-15T15:15:58Z
AUTHORS (10)
ABSTRACT
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with rapidly increasing world prevalence. In this paper, ability several statistical spectral features to detect AD from electroencephalographic (EEG) recordings evaluated. For purpose, clinical EEG 14 patients (8 mild 6 moderate AD) 10 healthy, age-matched individuals are analyzed. The signals initially segmented in nonoverlapping epochs different lengths ranging 5 s 12 s. Then, group calculated for each rhythm (δ, θ, α, β, γ) extracted, forming feature vector that trained tested Random Forests classifier. Six classification problems addressed, including discrimination whole-brain dynamics separately specific brain regions order highlight any alterations cortical regions. results indicated high accuracy 88.79% 96.78% classification. Also, was higher at posterior central than frontal area right side temporal lobe all problems.
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