Dec 10, 2019 · This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and ...
This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more ...
Missing: Robust | Show results with:Robust
Jan 30, 2020 · In this paper, we propose a neural memory network. (NMN), which facilitates trainable neural plasticity for robust classification of seizure ...
This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more ...
This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more ...
Bibliographic details on Neural Memory Networks for Robust Classification of Seizure Type.
Apr 7, 2020 · The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures.
People also ask
What is the new classification of seizure types?
What type of seizure affects memory?
Do seizure meds cause memory loss?
Can seizures affect a person's emotions and memory?
We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions.
Current seizure detection methods based on deep learning usually rely on single convolutional neural network (CNN) or recurrent neural network (RNN) models.
A novel DL network for seizure-type classification, consisting of CNN and Bi-LSTM · The proposed MP-SeizNet was trained on different representations of EEG data.