{"id":{"repo_id":"vilnius","oai_identifier":"oai:vu.lt:elaba:25856436"},"canonical_url":"https://search.dev.ndltd.org/etd/vilnius/oai:vu.lt:elaba:25856436","repository":{"repo_id":"vilnius","name":"Vilnius University","base_url":"https://epublications.vu.lt/oai"},"display":{"title":"Rekurentinių diagramų tipo metodai elektroencefalogramų analizei /","abstract":"Recurrence plots are widely used in the analysis of complex dynamic systems. A good example of complex systems could be found in medicine where the signals encountered in the latter are usually nonlinear and chaotic. Variables of reccurence quantification analysis are widely used in the classification of complex signals. In this work reccurence quantification analysis is used to classify patients with rolandic epilepsy or structural focal epilepsy. Data for analysis was provided by Children‘s Hospital, Affiliate of Vilnius University Hospital Santaros Klinikos. There was an analysis of 8, 16, 32-sec-long electroencephalogram (EEG) fragments performed in this work. The statistical analysis showed differences between the two patient groups. The best classification result was achieved with EEG fragments of 16 s. SVM classifier showed an accuracy of 72\\%, neural network - 67\\%. Doctors distinguish two types of epilepsy by analyzing epileptiform discharges also known as spikes. In this work, the author tried to classify the epilepsy types by performing the quantitative analysis of spikes sequences. Spikes were detected in patient's EEG with a special algorithm and then spikes were gathered to one sequence. The dependency between the number of spikes in the sequence and the accuracy of the classification was not determined.","abstract_html":"Recurrence plots are widely used in the analysis of complex dynamic systems. A good example of complex systems could be found in medicine where the signals encountered in the latter are usually nonlinear and chaotic. Variables of reccurence quantification analysis are widely used in the classification of complex signals. In this work reccurence quantification analysis is used to classify patients with rolandic epilepsy or structural focal epilepsy. Data for analysis was provided by Children‘s Hospital, Affiliate of Vilnius University Hospital Santaros Klinikos. There was an analysis of 8, 16, 32-sec-long electroencephalogram (EEG) fragments performed in this work. The statistical analysis showed differences between the two patient groups. The best classification result was achieved with EEG fragments of 16 s. SVM classifier showed an accuracy of 72\\%, neural network - 67\\%. Doctors distinguish two types of epilepsy by analyzing epileptiform discharges also known as spikes. In this work, the author tried to classify the epilepsy types by performing the quantitative analysis of spikes sequences. Spikes were detected in patient's EEG with a special algorithm and then spikes were gathered to one sequence. The dependency between the number of spikes in the sequence and the accuracy of the classification was not determined.","abstract_has_math":false,"creators":["Molis, Marekas,"],"institution":"Institutional Repository of Vilnius University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Meškauskas, Tadas"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-24T05:55:48Z","subjects":[],"languages":["lit"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.vu.lt/VU:ELABAETD25856436&prefLang=en_US","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meškauskas, Tadas"]},{"key":"dc:creator","label":"Author","values":["Molis, Marekas,"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018"]},{"key":"dc:publisher","label":"Institution","values":["Institutional Repository of Vilnius University"]},{"key":"dc:relation","label":"Dc Relation","values":["https://epublications.vu.lt/object/elaba:25856436/25856436.pdf"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["lit"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.vu.lt/VU:ELABAETD25856436&prefLang=en_US"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Recurrence plots are widely used in the analysis of complex dynamic systems. A good example of complex systems could be found in medicine where the signals encountered in the latter are usually nonlinear and chaotic. Variables of reccurence quantification analysis are widely used in the classification of complex signals. In this work reccurence quantification analysis is used to classify patients with rolandic epilepsy or structural focal epilepsy. Data for analysis was provided by Children‘s Hospital, Affiliate of Vilnius University Hospital Santaros Klinikos. There was an analysis of 8, 16, 32-sec-long electroencephalogram (EEG) fragments performed in this work. The statistical analysis showed differences between the two patient groups. The best classification result was achieved with EEG fragments of 16 s. SVM classifier showed an accuracy of 72\\%, neural network - 67\\%. Doctors distinguish two types of epilepsy by analyzing epileptiform discharges also known as spikes. In this work, the author tried to classify the epilepsy types by performing the quantitative analysis of spikes sequences. Spikes were detected in patient's EEG with a special algorithm and then spikes were gathered to one sequence. The dependency between the number of spikes in the sequence and the accuracy of the classification was not determined."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Rekurentinių diagramų tipo metodai elektroencefalogramų analizei /","Methods of recurrence plot type for analysis of electroencephalograms."]}]}],"canonical_facts":{"dc:contributor":["Meškauskas, Tadas"],"dc:creator":["Molis, Marekas,"],"dc:date":["2018"],"dc:description":["Recurrence plots are widely used in the analysis of complex dynamic systems. A good example of complex systems could be found in medicine where the signals encountered in the latter are usually nonlinear and chaotic. Variables of reccurence quantification analysis are widely used in the classification of complex signals. In this work reccurence quantification analysis is used to classify patients with rolandic epilepsy or structural focal epilepsy. Data for analysis was provided by Children‘s Hospital, Affiliate of Vilnius University Hospital Santaros Klinikos. There was an analysis of 8, 16, 32-sec-long electroencephalogram (EEG) fragments performed in this work. The statistical analysis showed differences between the two patient groups. The best classification result was achieved with EEG fragments of 16 s. SVM classifier showed an accuracy of 72\\%, neural network - 67\\%. Doctors distinguish two types of epilepsy by analyzing epileptiform discharges also known as spikes. In this work, the author tried to classify the epilepsy types by performing the quantitative analysis of spikes sequences. Spikes were detected in patient's EEG with a special algorithm and then spikes were gathered to one sequence. The dependency between the number of spikes in the sequence and the accuracy of the classification was not determined."],"dc:format":["application/pdf"],"dc:identifier":["https://repository.vu.lt/VU:ELABAETD25856436&prefLang=en_US"],"dc:language":["lit"],"dc:publisher":["Institutional Repository of Vilnius University"],"dc:relation":["https://epublications.vu.lt/object/elaba:25856436/25856436.pdf"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:title":["Rekurentinių diagramų tipo metodai elektroencefalogramų analizei /","Methods of recurrence plot type for analysis of electroencephalograms."],"dc:type":["info:eu-repo/semantics/masterThesis"]},"updated_at":"2026-07-24T05:55:48Z"}