EEG seizure detection: concepts, techniques, challenges, and future trends
AA Ein Shoka, MM Dessouky, A El-Sayed… - Multimedia Tools and …, 2023 - Springer
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
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 …
An effective dual self-attention residual network for seizure prediction
X Yang, J Zhao, Q Sun, J Lu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
As one of the most challenging data analysis tasks in chronic brain diseases, epileptic
seizure prediction has attracted extensive attention from many researchers. Seizure …
seizure prediction has attracted extensive attention from many researchers. Seizure …
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 …
Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals
AA Ein Shoka, MH Alkinani, AS El-Sherbeny… - Brain Informatics, 2021 - Springer
Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure
using several methods such as neurological examination, blood tests, computerized …
using several methods such as neurological examination, blood tests, computerized …
[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 …
Classification of EEG signals for prediction of epileptic seizures
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a
single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal …
single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal …
A novel permutation entropy-based EEG channel selection for improving epileptic seizure prediction
The key research aspects of detecting and predicting epileptic seizures using
electroencephalography (EEG) signals are feature extraction and classification. This paper …
electroencephalography (EEG) signals are feature extraction and classification. This paper …