{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:17:40Z","timestamp":1777907860106,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684000","type":"print"},{"value":"9781643684017","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>In this study, we classify the seizure types using feature extraction and machine learning algorithms. Initially, we pre-processed the electroencephalogram (EEG) of focal non-specific seizure (FNSZ), generalized seizure (GNSZ), tonic-clonic seizure (TCSZ), complex partial seizure (CPSZ) and absence seizure (ABSZ). Further, 21 features from time (9) and frequency (12) domain were computed from the EEG signals of different seizure types. XGBoost classifier model was built for individual domain features and combination of time and frequency features and validated the results using 10-fold cross-validation. Our results revealed that the classifier model with combination of time and frequency features performed well followed by the time and frequency domain features. We obtained a highest multi-class accuracy of 79.72% for the classification of five types of seizure while using all the 21 features. The band power between 11-13 Hz was found to be the top feature in our study. The proposed study can be used for the seizure type classification in clinical applications.<\/jats:p>","DOI":"10.3233\/shti230426","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:51:53Z","timestamp":1688111513000},"source":"Crossref","is-referenced-by-count":2,"title":["Multi-Class Seizure Type Classification Using Features Extracted from the EEG"],"prefix":"10.3233","author":[{"given":"Abirami","family":"Selvaraj","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swarubini","family":"PJ","sequence":"additional","affiliation":[{"name":"Department of Sensor and Biomedical Technology, VIT University, Vellore campus, Vellore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Thomas","sequence":"additional","affiliation":[{"name":"Montreal Neurological Institute, McGill University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuvaraj","family":"Rajamanickam","sequence":"additional","affiliation":[{"name":"Science of Learning in Education Centre (SoLEC), National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramshekhar N.","family":"Menon","sequence":"additional","affiliation":[{"name":"Department of Neurology, R Madhavan Nayar Centre for Comprehensive Epilepsy Care, Cognition Behavioral Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5759-6632","authenticated-orcid":false,"given":"Jac Fredo","family":"Agastinose Ronickom","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:51:55Z","timestamp":1688111515000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230426","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}