EEG Signal Analysis Approaches for Epileptic Seizure Event Prediction Using Deep Learning
C Samara, E Vrochidou… - … Conference on Software …, 2023 - ieeexplore.ieee.org
Epilepsy is classified as one of the three most prevalent neurological disorders, alongside
strokes and migraines. It is characterized by the occurrence of epileptic seizures that can be …
strokes and migraines. It is characterized by the occurrence of epileptic seizures that can be …
Data augmentation for seizure prediction with generative diffusion model
Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-
based seizure prediction methods. However, existing DA approaches are just the linear …
based seizure prediction methods. However, existing DA approaches are just the linear …
Multiband seizure type classification based on 3D convolution with attention mechanisms
H Huang, P Chen, J Wen, X Lu, N Zhang - Computers in Biology and …, 2023 - Elsevier
Electroencephalogram (EEG) signal contains important information about abnormal brain
activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy …
activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy …
Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction
Abstract Domain adaptation (DA) has been frequently used to solve the inter-patient
variability problem in EEG-based seizure prediction. However, existing DA methods require …
variability problem in EEG-based seizure prediction. However, existing DA methods require …
A self-interpretable deep learning model for seizure prediction using a multi-scale prototypical part network
The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of
seizures, which is crucial to improving patients' quality of life. Many deep learning-based …
seizures, which is crucial to improving patients' quality of life. Many deep learning-based …
Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction
Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and
accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make …
accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make …
An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning
Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals
with epilepsy. Over recent years, a multitude of deep learning-based approaches have …
with epilepsy. Over recent years, a multitude of deep learning-based approaches have …
Multi-perspective characterization of seizure prediction based on microstate analysis
W Shi, Y Cao, F Chen, W Tong, L Zhang… - Frontiers in …, 2024 - frontiersin.org
Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the
affected individual's social functionality and quality of life. This study aims to integrate …
affected individual's social functionality and quality of life. This study aims to integrate …
Early diagnosis of epileptic seizures over eeg signals using deep learning approach
E Özer - 2023 - search.proquest.com
Epilepsy, one of the most common neurological diseases, occurs with sudden electrical
discharges and may manifest itself in the form of seizures. Epileptic seizures can occur in a …
discharges and may manifest itself in the form of seizures. Epileptic seizures can occur in a …
Unsupervised and Self-Supervised Machine-Learning for Epilepsy Detection on EEG Data
L Benfenati - 2023 - webthesis.biblio.polito.it
Epilepsy is a neurological disorder characterized by abnormal electrical activity of the brain
that causes recurrent seizures. Electroencephalography (EEG) data can help in the …
that causes recurrent seizures. Electroencephalography (EEG) data can help in the …