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
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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