Toward automated prediction of sudden unexpected death in epilepsy
Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked
complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to …
complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to …
[HTML][HTML] Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images
We hypothesized that expert epileptologists can detect seizures directly by visually
analyzing EEG plot images, unlike automated methods that analyze spectro-temporal …
analyzing EEG plot images, unlike automated methods that analyze spectro-temporal …
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 …
Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures
A new methodology is presented for (a) detecting,(b) locating, and (c) quantifying the
damage severity in a smart highrise building structure. The methodology consists of three …
damage severity in a smart highrise building structure. The methodology consists of three …
Automatic seizure detection based on S-transform and deep convolutional neural network
Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the
massive workload of reviewing continuous EEGs. In this work, a novel approach, combining …
massive workload of reviewing continuous EEGs. In this work, a novel approach, combining …
Multi-biosignal analysis for epileptic seizure monitoring
Persons who suffer from intractable seizures are safer if attended when seizures strike.
Consequently, there is a need for wearable devices capable of detecting both convulsive …
Consequently, there is a need for wearable devices capable of detecting both convulsive …
Early seizure detection algorithm based on intracranial EEG and random forest classification
The goal of this study is to provide a seizure detection algorithm that is relatively simple to
implement on a microcontroller, so it can be used for an implantable closed loop stimulation …
implement on a microcontroller, so it can be used for an implantable closed loop stimulation …
A deep fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
Epileptic seizure detection with an end-to-end temporal convolutional network and bidirectional long short-term memory model
Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …
Hybrid attention network for epileptic EEG classification
Y Zhao, J He, F Zhu, T **ao, Y Zhang… - … Journal of Neural …, 2023 - World Scientific
Automatic seizure detection from electroencephalography (EEG) based on deep learning
has been significantly improved. However, existing works have not adequately excavate the …
has been significantly improved. However, existing works have not adequately excavate the …