[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 …
Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …
[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 …
Self-supervised graph neural networks for improved electroencephalographic seizure analysis
Automated seizure detection and classification from electroencephalography (EEG) can
greatly improve seizure diagnosis and treatment. However, several modeling challenges …
greatly improve seizure diagnosis and treatment. However, several modeling challenges …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
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 …
Interactive local and global feature coupling for EEG-based epileptic seizure detection
Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate
the progress of epilepsy diagnosis. Current seizure detection methods based on deep …
the progress of epilepsy diagnosis. Current seizure detection methods based on deep …
Hierarchy graph convolution network and tree classification for epileptic detection on electroencephalography signals
The epileptic detection with electroencephalography (EEG) has been deeply studied and
developed. However, previous research gave little attention to the physical appearance and …
developed. However, previous research gave little attention to the physical appearance and …
Automated inter-patient seizure detection using multichannel convolutional and recurrent neural networks
We present an end-to-end deep learning model that can automatically detect epileptic
seizures in multichannel electroencephalography (EEG) recordings. Our model combines a …
seizures in multichannel electroencephalography (EEG) recordings. Our model combines a …
A self-attention model for cross-subject seizure detection
Epilepsy is a neurological disorder characterized by recurring seizures, detected by
electroencephalography (EEG). EEG signals can be detected by manual time-consuming …
electroencephalography (EEG). EEG signals can be detected by manual time-consuming …
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
We present a unified framework called deep dependency networks (DDNs) that combines
dependency networks and deep learning architectures for multi-label classification, with a …
dependency networks and deep learning architectures for multi-label classification, with a …