This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more ...
In this paper, we propose a neural memory network (NMN), which facilitates trainable neural plasticity for robust classification of seizure types.
Dec 10, 2019 · This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and ...
Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological ...
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Jan 10, 2024 · This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN).
Dec 5, 2022 · It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals.
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.
Jul 30, 2024 · In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and ...
A novel seizure detection method based on the deep bidirectional long short-term memory (Bi-LSTM) network is proposed in this paper.
Oct 16, 2023 · In this article, we employ the Superlet Transform (SLT) in conjunction with a deep convolutional neural network, specifically VGG-19, for the ...