Seizure prediction using directed transfer function and convolution neural network on intracranial EEG
Automatic seizure prediction promotes the development of closed-loop treatment system on
intractable epilepsy. In this study, by considering the specific information exchange between …
intractable epilepsy. In this study, by considering the specific information exchange between …
One-dimensional convolutional neural networks combined with channel selection strategy for seizure prediction using long-term intracranial EEG
Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an
increasing attention during recent years. iEEG signals are commonly recorded in the form of …
increasing attention during recent years. iEEG signals are commonly recorded in the form of …
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 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 …
Human intracranial EEG quantitative analysis and automatic feature learning for epileptic seizure prediction
Objective: The aim of this study is to develop an efficient and reliable epileptic seizure
prediction system using intracranial EEG (iEEG) data, especially for people with drug …
prediction system using intracranial EEG (iEEG) data, especially for people with drug …
Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram
Seizure prediction has attracted growing attention as one of the most challenging predictive
data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic …
data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic …
Epileptic seizure prediction using deep neural networks via transfer learning and multi-feature fusion
Z Yu, L Albera, R Le Bouquin Jeannes… - … journal of neural …, 2022 - World Scientific
Epilepsy is one of the most common neurological diseases, which can seriously affect the
patient's psychological well-being and quality of life. An accurate and reliable seizure …
patient's psychological well-being and quality of life. An accurate and reliable seizure …
Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm
M Sun, F Wang, T Min, T Zang, Y Wang - IEEE access, 2018 - ieeexplore.ieee.org
Accurate forecasting of high-risk clinical symptoms, like epileptic seizures, has the potential
to transform clinical epilepsy care and to create new therapeutic strategies for individuals in …
to transform clinical epilepsy care and to create new therapeutic strategies for individuals in …
An end-to-end deep learning approach for epileptic seizure prediction
An accurate seizure prediction system enables early warnings before seizure onset of
epileptic patients. It is extremely important for drug-refractory patients. Conventional seizure …
epileptic patients. It is extremely important for drug-refractory patients. Conventional seizure …
[HTML][HTML] Deep learning models for predicting epileptic seizures using iEEG signals
O Ouichka, A Echtioui, H Hamam - Electronics, 2022 - mdpi.com
Epilepsy is a chronic neurological disease characterized by a large electrical explosion that
is excessive and uncontrolled, as defined by the world health organization. It is an anomaly …
is excessive and uncontrolled, as defined by the world health organization. It is an anomaly …