Automated seizure prediction
In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
[HTML][HTML] Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies
Patients suffering from epileptic seizures are usually treated with medication and/or surgical
procedures. However, in more than 30% of cases, medication or surgery does not effectively …
procedures. However, in more than 30% of cases, medication or surgery does not effectively …
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 …
Epileptic seizures prediction using deep learning techniques
Epilepsy is a very common neurological disease that has affected more than 65 million
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …
Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach
Epileptic seizures occur as a result of a process that develops over time and space in
epileptic networks. In this study, we aim at developing a generalizable method for patient …
epileptic networks. In this study, we aim at developing a generalizable method for patient …
Epileptic seizure prediction using scalp electroencephalogram signals
Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of
patients affected with epilepsy cannot be treated with medicines/surgical procedures …
patients affected with epilepsy cannot be treated with medicines/surgical procedures …
Dynamic learning framework for epileptic seizure prediction using sparsity based EEG reconstruction with optimized CNN classifier
BP Prathaban, R Balasubramanian - Expert Systems with Applications, 2021 - Elsevier
Abstract The World Health Organization (WHO) recently stated that epilepsy affects nearly
65 million people of the world population. Early forecast of the oncoming seizures is of …
65 million people of the world population. Early forecast of the oncoming seizures is of …
A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement
It is critical to determine whether the brain state of an epilepsy patient is indicative of a
possible seizure onset; thus, appropriate therapy or alarm may be delivered in time …
possible seizure onset; thus, appropriate therapy or alarm may be delivered in time …
Seizure prediction in EEG signals using STFT and domain adaptation
P Peng, Y Song, L Yang, H Wei - Frontiers in Neuroscience, 2022 - frontiersin.org
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional approaches commonly collect training and testing samples …
resistant epilepsy. Conventional approaches commonly collect training and testing samples …
A deep fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power
P Peng, L Xie, H Wei - International journal of neural systems, 2021 - World Scientific
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …