{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/89052"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/89052","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Prediction of canine epilepsy","abstract":"Seizure prediction is a problem in biomedical science which now is possible to solve with machine learning methods. A seizure prediction system has the power to assist those affected by epilepsy in better managing their medication, daily activities and improving the quality of life. Usage of machine learning algorithms and the availability of long term Intracranial Electroencephalographic (iEEG) recordings have tremendously reduced the complications involved in the challenging seizure prediction problem. Data, in the form of iEEG was collected from canines with naturally occurring epilepsy for the analysis and a seizure prediction system consisting of a machine learning based pipeline was implemented to generate seizure warnings when potential preictal activity is observed in the iEEG recording. A comparison between the different extracted features, dimensionality reduction techniques, and machine learning techniques was performed to investigate the relative effectiveness of the different techniques in the application of seizure prediction. The machine learning protocol performed significantly better than a chance prediction algorithm in all the analyzed subjects. Moreover, the analysis revealed subject-specific neurophysiological changes in the extracted features prior to lead seizures suggesting the existence of a distinct, identifiable preictal state.","abstract_html":"Seizure prediction is a problem in biomedical science which now is possible to solve with machine learning methods. A seizure prediction system has the power to assist those affected by epilepsy in better managing their medication, daily activities and improving the quality of life. Usage of machine learning algorithms and the availability of long term Intracranial Electroencephalographic (iEEG) recordings have tremendously reduced the complications involved in the challenging seizure prediction problem. Data, in the form of iEEG was collected from canines with naturally occurring epilepsy for the analysis and a seizure prediction system consisting of a machine learning based pipeline was implemented to generate seizure warnings when potential preictal activity is observed in the iEEG recording. A comparison between the different extracted features, dimensionality reduction techniques, and machine learning techniques was performed to investigate the relative effectiveness of the different techniques in the application of seizure prediction. The machine learning protocol performed significantly better than a chance prediction algorithm in all the analyzed subjects. Moreover, the analysis revealed subject-specific neurophysiological changes in the extracted features prior to lead seizures suggesting the existence of a distinct, identifiable preictal state.","abstract_has_math":false,"creators":["Varatharajah, Yogatheesan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Iyer, Ravishankar K.","Kalbarczyk, Zbigniew T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-02T19:34:22Z","date_published":"2016-03-02T19:34:22Z","updated_at":"2026-07-22T22:26:32Z","subjects":["Seizure prediction","Canine epilepsy","Preictal state","Prediction pipeline","Machine learning","Dimensionality Reduction"],"languages":["en"],"rights":["Copyright 2015 Yogatheesan Varatharajah"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/89052","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K.","Kalbarczyk, Zbigniew T."]},{"key":"dc:creator","label":"Author","values":["Varatharajah, Yogatheesan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-03-02T19:34:22Z","2015-12-07","2015-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Seizure prediction","Canine epilepsy","Preictal state","Prediction pipeline","Machine learning","Dimensionality Reduction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Yogatheesan Varatharajah"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/89052"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Seizure prediction is a problem in biomedical science which now is possible to solve with machine learning methods. 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The machine learning protocol performed significantly better than a chance prediction algorithm in all the analyzed subjects. Moreover, the analysis revealed subject-specific neurophysiological changes in the extracted features prior to lead seizures suggesting the existence of a distinct, identifiable preictal state.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Yogatheesan Varatharajah, accepted the attached license on 2015-12-04 at 14:26.","The student, Yogatheesan Varatharajah, submitted this Thesis for approval on 2015-12-04 at 14:35.","This Thesis was approved for publication on 2015-12-07 at 11:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8938 on 2016-03-02 at 12:51:29","Made available in DSpace on 2016-03-02T19:34:22Z (GMT). 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Usage of machine learning algorithms and the availability of long term Intracranial Electroencephalographic (iEEG) recordings have tremendously reduced the complications involved in the challenging seizure prediction problem. Data, in the form of iEEG was collected from canines with naturally occurring epilepsy for the analysis and a seizure prediction system consisting of a machine learning based pipeline was implemented to generate seizure warnings when potential preictal activity is observed in the iEEG recording. A comparison between the different extracted features, dimensionality reduction techniques, and machine learning techniques was performed to investigate the relative effectiveness of the different techniques in the application of seizure prediction. The machine learning protocol performed significantly better than a chance prediction algorithm in all the analyzed subjects. Moreover, the analysis revealed subject-specific neurophysiological changes in the extracted features prior to lead seizures suggesting the existence of a distinct, identifiable preictal state.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Yogatheesan Varatharajah, accepted the attached license on 2015-12-04 at 14:26.","The student, Yogatheesan Varatharajah, submitted this Thesis for approval on 2015-12-04 at 14:35.","This Thesis was approved for publication on 2015-12-07 at 11:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8938 on 2016-03-02 at 12:51:29","Made available in DSpace on 2016-03-02T19:34:22Z (GMT). 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