Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

• Published in: Neural computing & applications Vol. 36; no. 6; pp. 2835 - 2852
• Main Authors: Omar, Ahmed, Abd El-Hafeez, Tarek
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2024; Springer Nature B.V
• Subjects: 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

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...