| Autor: |
Voruganti, Nageshwar, Gurrala, Vijaya Kumar |
| Předmět: |
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| Zdroj: |
International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 4, p970-980, 11p |
| Abstrakt: |
The neural activities of the brain are detected by Electroencephalography (EEG) that allows for analysis and classification of epileptic disease. The existing methods fail to capture high-dimensional data between adjacent sequences which make it difficult for the classifier to process and maximize the classification errors. This research proposes Hierarchical Long Short-Term Memory (H-LSTM) with a skip connection-based epileptic seizure classification method. The H- LSTM captures both short and long-term dependencies between adjacent sequences in the high-dimensional data. The skip connection is introduced between H- LSTM layers, facilitating the flow of data across adjacent sequences to improve the classification performance of epileptic seizures. The datasets used to evaluate the proposed H- LSTM with skip connection-based classification method are BONN-EEG and CHB-MIT EEG. The proposed H-LSTM with skip connection method attains 99.81% accuracy on BONN - EEG while attaining 99.34% accuracy on the CHB-MIT EEG dataset which is more effective than the existing methods namely, Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Network (GCN). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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