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 Book Chapter Faculty Publications- Book Chapter 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 Kumar, Basant; Najmusseher; Nizar Banu, P.K.; Dwivedi, Rashmi Title A name given to the resource Epileptic Seizure Detection Contribution in Healthcare Sustainability 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 Studies in Systems, Decision and Control;Volume;601;pp.225-235 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-92240-4_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-92240-4_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014173618?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014173618?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 Kumar B., Modern College of Business and Science, Muscat, Oman; Najmusseher, Department of Computer Science, CHRIST University Central Campus, Karnataka, Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST University Central Campus, Karnataka, Bangalore, India; Dwivedi R., Muscat University, Muscat, Oman Description An account of the resource This study describes a sustainable EEG data methodology. Classification using Discrete Wavelet Transform (DWT) for feature extraction, with the objective of reducing the computational efforts while keeping accurate neural signal analysis. DWT decomposes the EEG signal into timefrequency specific components which allows extraction of ten key wavelet features, including wavelet energy, entropy, maximal coefficients, zero-crossing counts, and dominant frequency. These features capture essential timefrequency features of EEG signals, providing a comprehensive yet computationally efficient representation. By streamlining feature extraction, this approach reduces data dimensionality and minimizes computational processing time, aligning with sustainable technology objectives. The resulting feature vectors serve as robust inputs for classification models, effectively supporting EEG data interpretation with reduced energy and less resource utilization. This study demonstrates that targeted feature extraction can achieve high classification performance in EEG analysis while adhering to principles of sustainability and resource efficiency. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource EEG; Epilepsy; Seizures; Signal transformation; Wavelet Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21984182; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online