A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals
The electroencephalogram (EEG) is the most prominent means to study epilepsy and
capture changes in electrical brain activity that could declare an imminent seizure. In this …
capture changes in electrical brain activity that could declare an imminent seizure. In this …
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 …
Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network
Epilepsy seizure prediction paves the way of timely warning for patients to take more active
and effective intervention measures. Compared to seizure detection that only identifies the …
and effective intervention measures. Compared to seizure detection that only identifies the …
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 …
Epileptic seizure prediction using CSP and LDA for scalp EEG signals
TN Alotaiby, SA Alshebeili, FM Alotaibi… - Computational …, 2017 - Wiley Online Library
This paper presents a patient‐specific epileptic seizure predication method relying on the
common spatial pattern‐(CSP‐) based feature extraction of scalp electroencephalogram …
common spatial pattern‐(CSP‐) based feature extraction of scalp electroencephalogram …
The performance evaluation of the state-of-the-art EEG-based seizure prediction models
Z Ren, X Han, B Wang - Frontiers in Neurology, 2022 - frontiersin.org
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and
even death. The rapid development of electroencephalogram (EEG) and Artificial …
even death. The rapid development of electroencephalogram (EEG) and Artificial …
Exploring the applicability of transfer learning and feature engineering in epilepsy prediction using hybrid transformer model
Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
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 …