An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …

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 …

Multi-channel vision transformer for epileptic seizure prediction

R Hussein, S Lee, R Ward - Biomedicines, 2022 - mdpi.com
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of
awareness. Around 30% of epileptic patients continue to have seizures despite taking anti …

An epileptic seizure prediction method based on CBAM-3D CNN-LSTM model

X Lu, A Wen, L Sun, H Wang, Y Guo… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Epilepsy as a common disease of the nervous system, with high incidence, sudden and
recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment …

An efficient epilepsy prediction model on european dataset with model evaluation considering seizure types

SM Varnosfaderani, I McNulty… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
This paper develops a computationally efficient model for automatic patient-specific seizure
prediction using a two-layer LSTM from multichannel intracranial electroencephalogram …

A Bimodal Closed-Loop Neuromodulation Implant Integrated with Ultraflexible Probes to Treat Epilepsy

G Li, Y Tian, L Jiang, S Jin, Y Ye, Y Lu, H Su… - Biosensors and …, 2024 - Elsevier
Anti-seizure medications and deep brain stimulation are widely used therapies to treat
seizures; however, both face limitations such as resistance and the unpredictable nature of …

Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction

JD Chambers, MJ Cook, AN Burkitt… - Frontiers in …, 2024 - frontiersin.org
Objective Seizure prediction could improve quality of life for patients through removing
uncertainty and providing an opportunity for acute treatments. Most seizure prediction …

Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data

M Khalid, A Raza, A Akhtar, F Rustam… - Digital …, 2024 - journals.sagepub.com
Objective Epileptic seizures are neurological events that pose significant risks of physical
injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often …

Analysis of artifacts removal techniques in EEG signals for energy-constrained devices

I McNulty, SM Varnosfaderani, O Makke… - … on Circuits and …, 2021 - ieeexplore.ieee.org
This paper analyzes and evaluates various denoising techniques, including Wavelet
Transform and Moving Average Filter methods for removing ocular and motion artifacts from …

General and patient-specific seizure classification using deep neural networks

YM Massoud, M Abdelzaher, L Kuhlmann… - … Integrated Circuits and …, 2023 - Springer
Seizure prediction algorithms have been central in the field of data analysis for the
improvement of epileptic patients' lives. The most recent advancements of which include the …