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 …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
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 …
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
Abstract Background and Objective Neonatal seizures are the most common clinical
presentation of neurological conditions and can have adverse effects on the …
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
Functional connectivity of the human brain, representing statistical dependence of
information flow between cortical regions, significantly contributes to the study of the intrinsic …
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
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 …
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 …
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
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 …
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …
A spatiotemporal graph attention network based on synchronization for epileptic seizure prediction
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 …
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 …
the measurement of electrical brain activity which is extensively used in some areas of …