Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/5615
Title: Detection of Alzheimer's Dementia by Using Eeg Feature Maps and Deep Learning
Authors: Akbugday, Sude Pehlivan
Cura, Ozlem Karabiber
Akbugday, Burak
Akan, Aydin
Keywords: Alzheimer'S Dementia (Ad)
Eeg Feature Maps (Eeg-Fm)
Deep Learning
Cnn
Publisher: IEEE
Series/Report no.: European Signal Processing Conference
Abstract: One of the most frequent neurological conditions that impair cognitive abilities and have a major negative impact on quality of life is dementia. In this work, a novel approach for identifying Alzheimer's disease (AD) by utilizing electroencephalogram (EEG) signals via signal processing techniques is proposed. Five spectral domain characteristics are computed for one-minute EEG segment duration using EEG data. Each feature is mapped onto a 9 x 9 matrix called topographic EEG feature maps (EEG-FM) to represent spectral as well as spatial information on the same image. Images were then classified using a 2-layer convolutional neural network (CNN) to classify healthy and AD cases. Results indicate that the constructed CNN generalizes well, and the proposed method can accurately classify AD from EEG-FMs with up to %99 accuracy, precision, and recall with loss values as low as 0.01.
URI: https://doi.org/10.23919/EUSIPCO63174.2024.10714940
ISBN: 9789464593617
9798331519773
ISSN: 2076-1465
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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