Opportunities and challenges in develo** deep learning models using electronic health records data: a systematic review
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
[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 …
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
Since the manual detection of electrographic seizures in continuous electroencephalogram
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
A deep learning approach for automatic seizure detection in children with epilepsy
A Abdelhameed, M Bayoumi - Frontiers in Computational …, 2021 - frontiersin.org
Over the last few decades, electroencephalogram (EEG) has become one of the most vital
tools used by physicians to diagnose several neurological disorders of the human brain and …
tools used by physicians to diagnose several neurological disorders of the human brain and …
A multi-view deep learning framework for EEG seizure detection
The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …
Epileptic seizures prediction using deep learning techniques
Epilepsy is a very common neurological disease that has affected more than 65 million
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …
Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
Objective Automatic detection of epileptic seizures based on deep learning methods
received much attention last year. However, the potential of deep neural networks in seizure …
received much attention last year. However, the potential of deep neural networks in seizure …
Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques
Misdiagnosis of epilepsy is more seen in manual analysis of electroencephalogram (EEG)
signals for epileptic seizure event detection. Therefore, automated systems for epilepsy …
signals for epileptic seizure event detection. Therefore, automated systems for epilepsy …
[HTML][HTML] One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …