Generative adversarial networks in EEG analysis: an overview
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …
engineering applications. However, one of the challenges associated with recording EEG …
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 …
Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features
K Singh, J Malhotra - Complex & Intelligent Systems, 2022 - Springer
Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …
A hybrid deep learning approach for epileptic seizure detection in EEG signals
Early detection and proper treatment of epilepsy is essential and meaningful to those who
suffer from this disease. The adoption of deep learning (DL) techniques for automated …
suffer from this disease. The adoption of deep learning (DL) techniques for automated …
Seizure detection algorithm based on improved functional brain network structure feature extraction
L Jiang, J He, H Pan, D Wu, T Jiang, J Liu - Biomedical Signal Processing …, 2023 - Elsevier
Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic
seizures is essential for treatment. A seizure detection method with the structure of functional …
seizures is essential for treatment. A seizure detection method with the structure of functional …
Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals
Epilepsy is a neurological disorder that has severely affected many people's lives across the
world. Electroencephalogram (EEG) signals are used to characterize the brain's state and …
world. Electroencephalogram (EEG) signals are used to characterize the brain's state and …
D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism
AA Khan, RK Madendran… - CAAI Transactions …, 2023 - Wiley Online Library
Epilepsy is considered as a serious brain disorder in which patients frequently experience
seizures. The seizures are defined as the unexpected electrical changes in brain neural …
seizures. The seizures are defined as the unexpected electrical changes in brain neural …
Spatio-temporal MLP network for seizure prediction using EEG signals
In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
Methodological issues in evaluating machine learning models for EEG seizure prediction: Good cross-validation accuracy does not guarantee generalization to new …
There is an increasing interest in applying artificial intelligence techniques to forecast
epileptic seizures. In particular, machine learning algorithms could extract nonlinear …
epileptic seizures. In particular, machine learning algorithms could extract nonlinear …