Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events

Simple item page
dc.contributor.authorSani Saminu
dc.contributor.authorAdamu Halilu Jabire
dc.contributor.authorHajara Abdulkarim Aliyu
dc.contributor.authorAdamu Ya’u Iliyasu
dc.contributor.authorSuleiman Abimbola Yahaya
dc.contributor.authorMorufu Olusola Ibitoye
dc.contributor.authorGuizhi Xu
dc.date.accessioned2024-04-16T12:04:10Z
dc.date.available2024-04-16T12:04:10Z
dc.date.issued2024
dc.description.abstractPrediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life. Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked seizures and may occur without prior notice. Therefore, preventive measure that monitor and alert the possible occurrence of the seizures is paramount. Commercial and clinical available epileptic seizure computer aided detection system that utilized deep learning algorithms suffers from many challenges. These challenges ranges from low accuracy and precision, sensitive to artifacts and noise, among others. To enhance and increase the accuracy and optimal performance of these networks, this paper endeavor to investigate various optimization algorithm to optimized the network components and parameters in the developed incremental Principal Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on the big data scenario. The model proved to be effective in the characterization of seven seizure events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in optimization functions, activation functions, initialization functions, and regularisation techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained for each of the mentioned core components receptively.
dc.identifier.citationSaminu S., Jabire, A. H., Aliyu, H. A., Yahaya, S. A., Iliyasu, A. Y., Ibitoye, M. O., and Xu G. (2023): Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events, Bima Journal of Science and Technology 7 (4); 273-283, Published by Faculty of Science, Gombe State University (GSU).
dc.identifier.urihttps://journals.gjbeacademia.com/index.php/bimajst/article/view/551
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/11839
dc.language.isoen
dc.publisherBima Journal of Science and Technology
dc.subjectInvestigation
dc.subjectIncremental PCA-based LSTM Network
dc.subjectEpileptic Seizure Events
dc.titleInvestigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
BIMA_551-Article Text-1537-1-10-20240112.pdf
Size:
1.54 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections