… with brain network features, this paper has developed a simple automatic seizure detection
… , which can reduce the workload of EEG technicians in reading EEG and marking seizures. …

… learning-based seizure detection algorithms using combinations of different input forms …
network structures to systematically investigate how the input modalities and network structures …

… In this paper we proposed the temporal graph convolutional network (TGCN), a deep
learning model for structural time series. Unlike other approaches for analyzing time series data, …

… A novel epileptic seizure detection method is presented in this paper. Firstly, the
dimensionality of EEG signals is reduced. Reducing the dimensionality of EEG signals before …

… For addressing the specific problems, several different MLPNN structures have been tried.
The network structure that gave the best results for three classification problems of this work is…

… neural network (CNN) structure that can perform well the seizure … for seizure detection task
compared to Integer-Net. With 4-bit, Integer-Net surpasses BW model in seizure detection task …

… input modalities for analyzing a combination of network structures that would be suitable
for seizure … 3, shows the network structure of the ANN used for epileptic seizure detection. …

… cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE
Trans Biomed Eng, 55 (… Wavelet/mixture of experts network structure for EEG classification. …

… However, there are limitations for such seizure detection … [3] and various new network
structures have largely prompted the … have been proposed for seizure detection. Hugle et. al. …

… In the event of a seizure, in addition to the importance of information in an individual … for
seizure detection and results of classifiers are combined to achieve different modular structures. …