{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:45:16Z","timestamp":1761648316420,"version":"build-2065373602"},"reference-count":94,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Amrita Vishwa Vidyapeetham University, Coimbatore"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Epilepsy is a brain disorder that affects individuals; hence, preemptive diagnosis is required. Accurate classification of seizures is critical to optimize the treatment of epilepsy. Patients with epilepsy are unable to lead normal lives due to the unpredictable nature of seizures. Thus, developing new methods to help these patients can significantly improve their quality of life and result in huge financial savings for the healthcare industry. This paper presents a hybrid method integrating dynamic mode decomposition (DMD) and wavelet scattering transform (WST) for EEG-based seizure analysis. DMD allows for the breakdown of EEG signals into modes that catch the dynamical structures present in the EEG. Then, WST is applied as it is invariant to time-warping and computes robust hierarchical features at different timescales. DMD-WST combination provides an in-depth multi-scale analysis of the temporal structures present within the EEG data. This process improves the representation quality for feature extraction, which can convey dynamic modes and multi-scale frequency information for improved classification performance. The proposed hybrid approach is validated with three datasets, namely the CHB-MIT PhysioNet dataset, the Bern Barcelona dataset, and the Khas dataset, which can accurately distinguish the seizure and non-seizure states. The proposed method performed classification using different machine learning and deep learning methods, including support vector machine, random forest, k-nearest neighbours, booster algorithm, and bagging. These models were compared in terms of accuracy, precision, sensitivity, Cohen\u2019s kappa, and Matthew\u2019s correlation coefficient. The DMD-WST approach achieved a maximum accuracy of 99% and F1 score of 0.99 on the CHB-MIT dataset, and obtained 100% accuracy and F1 score of 1.00 on both the Bern Barcelona and Khas datasets, outperforming existing methods<\/jats:p>","DOI":"10.3390\/informatics12040117","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T09:16:07Z","timestamp":1761642967000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Approach Using Dynamic Mode Decomposition and Wavelet Scattering Transform for EEG-Based Seizure Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0079-550X","authenticated-orcid":false,"given":"Sreevidya","family":"C","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4853-8670","authenticated-orcid":false,"given":"Neethu","family":"Mohan","sequence":"additional","affiliation":[{"name":"Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3298-0539","authenticated-orcid":false,"given":"Sachin Kumar","family":"S","sequence":"additional","affiliation":[{"name":"Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aravind","family":"Harikumar","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO), Global Campaign Against Epilepsy (2005). 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