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 …
A deep learning based ensemble learning method for epileptic seizure prediction
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
[HTML][HTML] One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
Epileptic seizure detection by cascading isolation forest-based anomaly screening and EasyEnsemble
The electroencephalogram (EEG), for measuring the electrophysiological activity of the
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
FFT-based deep feature learning method for EEG classification
M Li, W Chen - Biomedical Signal Processing and Control, 2021 - Elsevier
This study introduces a new method for electroencephalogram (EEG) signal classification
based on deep learning model, by which relevant features are automatically learned in a …
based on deep learning model, by which relevant features are automatically learned in a …
Hierarchical Harris hawks optimization for epileptic seizure classification
Z Luo, S Jin, Z Li, H Huang, L Xiao, H Chen… - Computers in Biology …, 2022 - Elsevier
The intelligent recognition of electroencephalogram (EEG) signals is a valuable tool for
epileptic seizure classification. Given that visual inspection of EEG signals is time …
epileptic seizure classification. Given that visual inspection of EEG signals is time …
Patient-specific seizure prediction from electroencephalogram signal via multichannel feedback capsule network
In recent years, the use of convolutional neural networks (CNNs) has been common in
electroencephalogram (EEG)-based seizure prediction. However, CNNs lose local and …
electroencephalogram (EEG)-based seizure prediction. However, CNNs lose local and …
Capsule attention for multimodal EEG-EOG representation learning with application to driver vigilance estimation
Driver vigilance estimation is an important task for transportation safety. Wearable and
portable brain-computer interface devices provide a powerful means for real-time monitoring …
portable brain-computer interface devices provide a powerful means for real-time monitoring …
FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network
In this article, an optimized variational mode decomposition (OVMD), reduced deep
convolutional neural network (RDCNN), and multi-kernel random vector functional link …
convolutional neural network (RDCNN), and multi-kernel random vector functional link …