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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