A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease

A Modir, S Shamekhi, P Ghaderyan - Measurement, 2023 - Elsevier
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders in the
world. Although there is no known cure for it at the present, preventive drug trials and …

Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: from the perspective of event related potentials and deep …

C Wang, T Xu, W Yu, T Li, H Han, M Zhang… - International Journal of …, 2022 - Elsevier
Abstract Alzheimer's disease (AD), a neurodegenerative disorder characterized by
progressive cognitive decline, is generally prevalent in elderly people with significant …

Automatic detection of Alzheimer's disease from EEG signals using low-complexity orthogonal wavelet filter banks

DV Puri, SL Nalbalwar, AB Nandgaonkar… - … Signal Processing and …, 2023 - Elsevier
Background: Alzheimer's disease (AD) is one of the most common neurodegenerative
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …

Alzheimer's diseases diagnosis using fusion of high informative BiLSTM and CNN features of EEG signal

M Imani - Biomedical Signal Processing and Control, 2023 - Elsevier
Electroencephalography (EEG) signals are low cost and available data for diagnosis of
mental disorders such as Alzheimer's diseases (AD). Each EEG signal contains information …

A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals

W Xia, R Zhang, X Zhang, M Usman - Heliyon, 2023 - cell.com
Abstract Background The diagnosis of Alzheimer's disease (AD) using
electroencephalography (EEG) has garnered more attention recently. New methods In this …

EEGAlzheimer'sNet: Development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal

D kumar Ravikanti, S Saravanan - Biomedical Signal Processing and …, 2023 - Elsevier
A previous diagnosis of Alzheimer's disease (AD) in its initial stages is needed for patient
care because it helps patients adopt preventative measures before irreversible brain …

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions

M Parsa, HY Rad, H Vaezi, GA Hossein-Zadeh… - Computer Methods and …, 2023 - Elsevier
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly
progressed and gained popularity in recent years, but automatic feature extraction from EEG …

Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state

Y Chen, H Wang, D Zhang, L Zhang… - Frontiers in Neuroscience, 2023 - frontiersin.org
Introduction Diagnosing Alzheimer's disease (AD) lesions via visual examination of
Electroencephalography (EEG) signals poses a considerable challenge. This has prompted …

An effective convolutional neural network-based stacked long short-term memory approach for automated Alzheimer's disease prediction

S Saravanakumar, T Saravanan - Journal of Intelligent & …, 2022 - content.iospress.com
In today's world, Alzheimer's Disease (AD) is one of the prevalent neurological diseases
where early disease prediction can significantly enhance the compatibility of patient …

Deep neural network CSES-NET and multi-channel feature fusion for Alzheimer's disease diagnosis

J Qiao, M Zhang, Y Fan, K Fang, X Zhao… - … Signal Processing and …, 2024 - Elsevier
Alzheimer's disease (AD) is an irreversible brain disease. The structural Magnetic
Resonance Imaging (sMRI) has been widely used in the diagnosis of AD. However, the …