An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …

Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review

A Miltiadous, KD Tzimourta, N Giannakeas… - IEEE …, 2022 - ieeexplore.ieee.org
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …

EEG based depression recognition using improved graph convolutional neural network

J Zhu, C Jiang, J Chen, X Lin, R Yu, X Li… - Computers in Biology and …, 2022 - Elsevier
Depression is a global psychological disease that does serious harm to people. Traditional
diagnostic method of the doctor-patient communication, is not objective and accurate …

[HTML][HTML] A graph convolutional neural network for the automated detection of seizures in the neonatal EEG

K Raeisi, M Khazaei, P Croce, G Tamburro… - Computer methods and …, 2022 - Elsevier
Abstract Background and Objective Neonatal seizures are the most common clinical
presentation of neurological conditions and can have adverse effects on the …

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

X Shan, J Cao, S Huo, L Chen… - Human Brain …, 2022 - Wiley Online Library
Functional connectivity of the human brain, representing statistical dependence of
information flow between cortical regions, significantly contributes to the study of the intrinsic …

A class-imbalance aware and explainable spatio-temporal graph attention network for neonatal seizure detection

K Raeisi, M Khazaei, G Tamburro, P Croce… - International journal of …, 2023 - ricerca.unich.it
Seizures are the most prevalent clinical indication of neurological disorders in neonates. In
this study, a class-imbalance aware and explainable deep learning approach based on …

Interactive local and global feature coupling for EEG-based epileptic seizure detection

Y Zhao, D Chu, J He, M Xue, W Jia, F Xu… - … Signal Processing and …, 2023 - Elsevier
Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate
the progress of epilepsy diagnosis. Current seizure detection methods based on deep …

[HTML][HTML] Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases

S Abadal, P Galván, A Mármol, N Mammone… - Neural Networks, 2025 - Elsevier
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …

A spatiotemporal graph attention network based on synchronization for epileptic seizure prediction

Y Wang, Y Shi, Y Cheng, Z He, X Wei… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Accurate early prediction of epileptic seizures can provide timely treatment for patients.
Previous studies have mainly focused on a single temporal or spatial dimension, making it …

[HTML][HTML] A review of Graph Neural Networks for Electroencephalography data analysis

M Graña, I Morais-Quilez - Neurocomputing, 2023 - Elsevier
Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for
the measurement of electrical brain activity which is extensively used in some areas of …