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 …
Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.
D Liu, X Dong, D Bian, W Zhou - International Journal of Neural …, 2023 - europepmc.org
Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and
improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly …
improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly …
A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique
Early detection and proper treatment of epilepsy seizure is essential and meaningful to
those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing …
those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing …
Novel features extraction from eeg signals for epilepsy detection using machine learning model
Epilepsy is a neurological disorder that affects the brain, as well as the human body's nerves
and spinal cord, adversely causing unusual and uncontrollable behavior. This letter …
and spinal cord, adversely causing unusual and uncontrollable behavior. This letter …
CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique
MH Alshayeji - Machine Learning: Science and Technology, 2023 - iopscience.iop.org
Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's
way of life. Manual diagnosis is a laborious and time-consuming task prone to human error …
way of life. Manual diagnosis is a laborious and time-consuming task prone to human error …
Double Discrete Variational Autoencoder for Epileptic EEG Signals Classification
S Liang, X Zhang, H Zhao, Y Dang, R Hui… - IEEE Access, 2024 - ieeexplore.ieee.org
Electroencephalography (EEG) plays a key role in the clinical evaluation of epilepsy and
provides strong support for treatment decisions. However, analyzing and decoding EEG …
provides strong support for treatment decisions. However, analyzing and decoding EEG …
ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures
We proposed an Attentive Recurrent Neural Network (ARNN), which recurrently applies
attention layers along a sequence and has linear complexity with respect to the sequence …
attention layers along a sequence and has linear complexity with respect to the sequence …
A New Deep Learning Architecture Based on LSTM and Wavelet Transform for Epileptic EEG Signal Classification
Epilepsy poses a significant risk to human health. Numerous machine learning (ML) have
emerged to address this health challenge. Deep learning (DL), particularly recurrent neural …
emerged to address this health challenge. Deep learning (DL), particularly recurrent neural …