{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86631"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86631","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Hardware Implementation of Linear SVM Classifier for Epileptic Seizure Detection","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Elkoori Ghantala Karnam, Vinay; 0000-0002-4984-7451"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sanyal, Arindam","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:35:49Z","date_published":"2025-02-21T21:35:49Z","updated_at":"2026-07-27T19:05:32Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86631","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sanyal, Arindam","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Elkoori Ghantala Karnam, Vinay; 0000-0002-4984-7451"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:35:49Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86631"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Epilepsy is a neural disorder in a patient that is caused by the disorder in the functionality in the person's brain. This affects the central nervous system of the person, which leads to symptoms like jerking of limbs, loss of cognitive skills or loss of consciousness. Detection of these seizures is clinically very essential which can save the life of the patient in many cases. This work aims at development and Hardware implementation of a seizure detection algorithm. A linear Support Vector Machine (SVM) has been modelled to detect an epileptic seizure. In order to train and test the SVM, we used a publicly available dataset from Physionet.org which was recorded in Children's Hospital Boston with the help of researchers at the Massachusetts Institute of Technology (CHB-MIT Scalp EEG Dataset). For developing a sturdy SVM, different time-domain features were picked and evaluated individually for their performance to detect the epileptic seizures. After achieving promising results on the SVM model, we implemented the SVM into hardware with Digital Circuits. The digital hardware consists of all feature extractor circuits for the SVM as well as the SVM hardware. After testing the hardware implementation, we obtained an accuracy of over 95% that matched with the software model of the SVM.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hardware Implementation of Linear SVM Classifier for Epileptic Seizure Detection"]}]}],"canonical_facts":{"dc:contributor":["Sanyal, Arindam","Electrical Engineering"],"dc:creator":["Elkoori Ghantala Karnam, Vinay; 0000-0002-4984-7451"],"dc:date":["2025-02-21T21:35:49Z","2020"],"dc:description":["M.S.","Epilepsy is a neural disorder in a patient that is caused by the disorder in the functionality in the person's brain. This affects the central nervous system of the person, which leads to symptoms like jerking of limbs, loss of cognitive skills or loss of consciousness. Detection of these seizures is clinically very essential which can save the life of the patient in many cases. This work aims at development and Hardware implementation of a seizure detection algorithm. A linear Support Vector Machine (SVM) has been modelled to detect an epileptic seizure. In order to train and test the SVM, we used a publicly available dataset from Physionet.org which was recorded in Children's Hospital Boston with the help of researchers at the Massachusetts Institute of Technology (CHB-MIT Scalp EEG Dataset). For developing a sturdy SVM, different time-domain features were picked and evaluated individually for their performance to detect the epileptic seizures. After achieving promising results on the SVM model, we implemented the SVM into hardware with Digital Circuits. The digital hardware consists of all feature extractor circuits for the SVM as well as the SVM hardware. After testing the hardware implementation, we obtained an accuracy of over 95% that matched with the software model of the SVM.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86631"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["Hardware Implementation of Linear SVM Classifier for Epileptic Seizure Detection"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:32Z"}