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CLASSIFICATION OF DIFFERENT CAUSES OF DEMENTIA USING DEEP LEARNING TECHNIQUES ON EEG MEASUREMENTS
Abstract
Aims
Dementia is a neurocognitive syndrome that is caused by a variety of brain diseases, among which Alzheimer's disease (AD) is the most common. Other dementia types are vascular dementia (VAD), diffuse Lewy body dementia (DLB), frontotemporal lobe dementia (FTLD), Creutzfeldt Jakob disease (CJD), progressive supranuclear palsy (PSP), and mixed dementia (MXD). Electroencephalography (EEG) biomarkers are used to differentiate these dementia types, although often with limited success. The aim of this study is to investigate whether deep learning techniques can be used on EEG to create a classifier that can correctly diagnose the type of dementia.
Methods
The EEG data for this work includes measurements of 53 AD, 6 VAD, 11 DLB, 19 FTLD, 4 CJD, 2 PSP, and 7 MXD patients, together with the data from 83 healthy controls (HC). Deep learning is applied on the preprocessed epochs to build the classification model. The evaluation is done using holdout for the train-test split (50-33), and cross-validation for the validation set. The evaluation metrics are the AUPRC, accuracy, recall, and F1-score.
Results
The multi-class classification between AD, CJD, DLB, FTLD, MXD, and HC patients resulted in an AUPRC, accuracy, recall, and F1-score of respectively 85.3%, 90.9%, 65.1%, and 62.1%. The Dementia vs. No Dementia classification of this model resulted in a perfect classification of the 17 dementia patients and 16 HC of the test set.
Conclusions
The different type of dementia can be classified with a high accuracy using EEG deep learning.