[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
Seizure diaries and forecasting with wearables: epilepsy monitoring outside the clinic
It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by
infrequent seizures based on patient or caregiver reports and limited duration clinical …
infrequent seizures based on patient or caregiver reports and limited duration clinical …
A deep learning based ensemble learning method for epileptic seizure prediction
SM Usman, S Khalid, S Bashir - Computers in Biology and Medicine, 2021 - Elsevier
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 …
Detection of epileptic seizure using pretrained deep convolutional neural network and transfer learning
Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the
attending physician. However, the human factor may cause erroneous diagnosis in the …
attending physician. However, the human factor may cause erroneous diagnosis in the …
Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Background and objective Epilepsy is a prevalent disorder that affects the central nervous
system, causing seizures. In the current study, a novel algorithm is developed using …
system, causing seizures. In the current study, a novel algorithm is developed using …
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 …
Machine learning and wearable devices of the future
Abstract Machine learning (ML) is increasingly recognized as a useful tool in healthcare
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
The ability to forecast seizures minutes to hours in advance of an event has been verified
using invasive EEG devices, but has not been previously demonstrated using noninvasive …
using invasive EEG devices, but has not been previously demonstrated using noninvasive …
Seizure forecasting and cyclic control of seizures
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where
causes, underlying biomarkers, triggers, and patterns differ across individuals. The …
causes, underlying biomarkers, triggers, and patterns differ across individuals. The …
Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
S Saminu, G Xu, S Zhang… - Artificial Intelligence …, 2023 - ojs.bonviewpress.com
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …