[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 …
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 …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
[HTML][HTML] EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population
Background Epilepsy is one of the most common neurological conditions globally, and the
fourth most common in the United States. Recurrent non-provoked seizures characterize it …
fourth most common in the United States. Recurrent non-provoked seizures characterize it …
Spatio-temporal MLP network for seizure prediction using EEG signals
In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
A Hybrid DenseNet-LSTM model for epileptic seizure prediction
S Ryu, I Joe - Applied Sciences, 2021 - mdpi.com
The number of people diagnosed with epilepsy as a common brain disease accounts for
about 1% of the world's total population. Seizure prediction is an important study that can …
about 1% of the world's total population. Seizure prediction is an important study that can …
Seizure detection and prediction by parallel memristive convolutional neural networks
During the past two decades, epileptic seizure detection and prediction algorithms have
evolved rapidly. However, despite significant performance improvements, their hardware …
evolved rapidly. However, despite significant performance improvements, their hardware …
[HTML][HTML] Epileptic seizure detection from electroencephalogram (EEG) signals using linear graph convolutional network and DenseNet based hybrid framework
A clinical condition known as epilepsy occurs when the brain's regular electrical activity is
disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The …
disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The …
Peri‐ictal and non‐seizure EEG event detection using generated metadata
Lack of open access, seizure specific database has hindered the development of Automated
Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and …
Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and …
[PDF][PDF] Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals.
S Cherukuvada, R Kayalvizhi - Computers, Materials & Continua, 2023 - cdn.techscience.cn
The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early
identification of incoming seizures significantly impacts the lives of people with Epilepsy …
identification of incoming seizures significantly impacts the lives of people with Epilepsy …
Mical: mutual information-based cnn-aided learned factor graphs for seizure detection from eeg signals
B Salafian, EF Ben-Knaan, N Shlezinger… - Ieee …, 2023 - ieeexplore.ieee.org
We develop a hybrid model-based data-driven seizure detection algorithm called Mutual
Information-based CNN-Aided Learned factor graphs (MICAL) for detection of eclectic …
Information-based CNN-Aided Learned factor graphs (MICAL) for detection of eclectic …