Automatic seizure detection using fully convolutional nested LSTM
The automatic seizure detection system can effectively help doctors to monitor and diagnose
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
Patient-independent seizure detection based on channel-perturbation convolutional neural network and bidirectional long short-term memory
Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the
massive burden caused by manual inspection of long-term EEG. At present, most seizure …
massive burden caused by manual inspection of long-term EEG. At present, most seizure …
Scalp EEG classification using deep Bi-LSTM network for seizure detection
Automatic seizure detection technology not only reduces workloads of neurologists for
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
[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 …
Deep C-LSTM neural network for epileptic seizure and tumor detection using high-dimension EEG signals
Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis
of epilepsy and studying the human brain electrical activity. Previously, the traditional …
of epilepsy and studying the human brain electrical activity. Previously, the traditional …
Automatic seizure detection by convolutional neural networks with computational complexity analysis
Abstract Background and Objectives Nowadays, an automated computer-aided diagnosis
(CAD) is an approach that plays an important role in the detection of health issues. The main …
(CAD) is an approach that plays an important role in the detection of health issues. The main …
Epileptic-net: an improved epileptic seizure detection system using dense convolutional block with attention network from EEG
Epilepsy is a complex neurological condition that affects a large number of people
worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and …
worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and …
A lstm-cnn model for epileptic seizures detection using eeg signal
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
CNN based framework for detection of epileptic seizures
Epilepsy is a common neurological disease that uses electroencephalogram (EEG) data for
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
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 …