{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80015"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80015","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"EEG Time-Series Clustering Using ARMA Modeling and the Grassmannian","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Patil, Pratik; 0000-0001-9820-9838"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Slavakis, Konstantinos","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:11:52Z","date_published":"2019-07-30T15:11:52Z","updated_at":"2026-07-27T19:05:23Z","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/10477/80015","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Slavakis, Konstantinos","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Patil, Pratik; 0000-0001-9820-9838"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-07-30T15:11:52Z","2019","2019-05-17 09:33:25"]},{"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/10477/80015"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmical discharge for eithera focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of seizures highly recommended especially in long term EEG recordings.Technological advancement in noninvasive methods such as Electroencephalographic(EEG) helped doctors to analyze hour long EEG recordings to diagnose if patient is suffering from epilepsy or not. To help physicians diagnose seizure,we developed automatic seizure detection algorithm. We use model based feature extraction method, Auto Regressive Moving Average (ARMA) and use recently developed hypothesis from Riemannian Multi-Manifold Modeling to extract featuresin the Grassmannian. Later we use one of the hierarchical-clustering algorithm,Louvain method, to cluster the time series.This method is evaluated using real University of Bonn EEG dataset. Simulation results were promising as compare to State of Art clustering methods and outperformed them. An advantage of this method that no training data are used due to the unsupervised nature of clustering."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["EEG Time-Series Clustering Using ARMA Modeling and the Grassmannian"]}]}],"canonical_facts":{"dc:contributor":["Slavakis, Konstantinos","Electrical Engineering"],"dc:creator":["Patil, Pratik; 0000-0001-9820-9838"],"dc:date":["2019-07-30T15:11:52Z","2019","2019-05-17 09:33:25"],"dc:description":["M.S.","The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmical discharge for eithera focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of seizures highly recommended especially in long term EEG recordings.Technological advancement in noninvasive methods such as Electroencephalographic(EEG) helped doctors to analyze hour long EEG recordings to diagnose if patient is suffering from epilepsy or not. To help physicians diagnose seizure,we developed automatic seizure detection algorithm. We use model based feature extraction method, Auto Regressive Moving Average (ARMA) and use recently developed hypothesis from Riemannian Multi-Manifold Modeling to extract featuresin the Grassmannian. Later we use one of the hierarchical-clustering algorithm,Louvain method, to cluster the time series.This method is evaluated using real University of Bonn EEG dataset. Simulation results were promising as compare to State of Art clustering methods and outperformed them. An advantage of this method that no training data are used due to the unsupervised nature of clustering."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80015"],"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":["EEG Time-Series Clustering Using ARMA Modeling and the Grassmannian"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:23Z"}