Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Najmusseher; Banu, Nizar P.K. Title A name given to the resource Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Computing and Digital Systems;Volume;17;Issue;1; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.12785/ijcds/1571020131" target="_blank" rel="noreferrer noopener">https://doi.org/10.12785/ijcds/1571020131</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85217538815?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85217538815?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Najmusseher, Department, of Computer Science, CHRIST (Deemed to be University), Central Campus, Bangalore, 560029, India; Banu N.P.K., Department, of Computer Science, CHRIST (Deemed to be University), Central Campus, Bangalore, 560029, India Description An account of the resource Epileptic seizure, a severe neurological condition, profoundly impacts patients social lives, necessitating precise diagnosis for classification and prediction. This study addresses the need for reliable automated seizure detection in epilepsy by employing Artificial Intelligence (AI) driven analysis of Electroencephalography (EEG) signals. Key innovations include combining spectral and temporal features using Uniform Manifold Approximation and Projection (UMAP) with Fast Fourier Transformation (FFT), and the introduction of the Sequential Boosting Network (SeqBoostNet), a robust stacking model integrating machine learning and deep learning for effective seizure classification. Validated on benchmark datasets such as the BONN dataset from the UCI repository and the BEED from the Bangalore EEG Epilepsy Dataset, this approach achieved high accuracy, distinguishing Focal and Generalized seizure onsets with 95.91% accuracy and overall average accuracies of 96.71% on BEED and 97.11% on BONN. Existing models frequently struggle with the variability of seizure events. However, these findings underscore the models strength in distinguishing between seizure onset types, even with the inherent fluctuations in seizure patterns. This research not only advances automated seizure detection but also underscores the value of integrating AI with EEG analysis to improve neurological diagnostics, offering the potential for significant enhancements in diagnostic accuracy and patient outcomes. 2025 University of Bahrain. All rights reserved. Subject The topic of the resource AdaBoost; Deep Learning; Epileptic Seizure; FFT; LSTM; Machine Learning; UMAP Publisher An entity responsible for making the resource available University of Bahrain Relation A related resource ISSN: 2210142X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online