Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

Epilepsy is a widespread neurological disorder characterized by recurring seizures that have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is crucial for proper diagnosis and treatment. Deep learning models have shown promise in improving seizure recognit...

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Vydáno v:	Neural computing & applications Ročník 36; číslo 6; s. 2835 - 2852
Hlavní autoři:	Omar, Ahmed, Abd El-Hafeez, Tarek
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.02.2024 Springer Nature B.V
Témata:	Accuracy Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convulsions & seizures Data Mining and Knowledge Discovery Deep learning Electroencephalography Epilepsy Feature selection Image Processing and Computer Vision Machine learning Neurological diseases Optimization Original Article Principal components analysis Probability and Statistics in Computer Science Scaling Seizures Conv1D LSTM Epileptic seizure recognition Feature scaling EEG Chi-square feature selection GRU BiLSTM PCA
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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