Machine learning for predicting epileptic seizures using EEG signals: A review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …
researchers are striving towards employing these techniques for advancing clinical practice …
Seizure prediction—ready for a new era
Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming
majority of people with epilepsy regard the unpredictability of seizures as a major issue …
majority of people with epilepsy regard the unpredictability of seizures as a major issue …
Efficient epileptic seizure prediction based on deep learning
H Daoud, MA Bayoumi - IEEE transactions on biomedical …, 2019 - ieeexplore.ieee.org
Epilepsy is one of the world's most common neurological diseases. Early prediction of the
incoming seizures has a great influence on epileptic patients' life. In this paper, a novel …
incoming seizures has a great influence on epileptic patients' life. In this paper, a novel …
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 …
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 …
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 …
A deep convolutional neural network model for automated identification of abnormal EEG signals
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual
examination of these signals by experts is strenuous and time consuming. Hence, machine …
examination of these signals by experts is strenuous and time consuming. Hence, machine …
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] An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications
Recently, the rapid development of Artificial Intelligence (AI) applied in the Medical Internet
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …