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 …
[HTML][HTML] Interpreting deep learning models for epileptic seizure detection on EEG signals
Abstract While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-
ligence-based medical decision support, it remains sparsely implemented in clinical practice …
ligence-based medical decision support, it remains sparsely implemented in clinical practice …
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 …
[HTML][HTML] Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features
K Singh, J Malhotra - Complex & Intelligent Systems, 2022 - Springer
Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …
Epileptic seizure prediction with multi-view convolutional neural networks
The unpredictability of seizures is often considered by patients to be the most problematic
aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor …
aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor …
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 deep transformer model
The electroencephalogram (EEG) is the most promising and efficient technique to study
epilepsy and record all the electrical activity going in our brain. Automated screening of …
epilepsy and record all the electrical activity going in our brain. Automated screening of …
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in
combination with deep learning computational methods has received much attention in …
combination with deep learning computational methods has received much attention in …
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 …
Epileptic seizure forecasting with generative adversarial networks
Many outstanding studies have reported promising results in seizure forecasting, one of the
most challenging predictive data analysis problems. This is mainly because …
most challenging predictive data analysis problems. This is mainly because …