A distance-based dynamical transition analysis of time series signals and application to biological systems
dc.authorid | Alagoz, Serkan/0000-0003-2642-8462 | |
dc.authorid | Alagoz, Baris Baykant/0000-0001-5238-6433 | |
dc.authorwosid | Alagoz, Serkan/ABI-2130-2020 | |
dc.authorwosid | Alagoz, Baris Baykant/ABG-8526-2020 | |
dc.contributor.author | Alagoz, Serkan | |
dc.contributor.author | Alagoz, Baris Baykant | |
dc.date.accessioned | 2024-08-04T20:36:01Z | |
dc.date.available | 2024-08-04T20:36:01Z | |
dc.date.issued | 2012 | |
dc.department | İnönü Üniversitesi | en_US |
dc.description.abstract | This study demonstrates an application of distance-based numerical measures to the phase space of time series signals, in order to obtain a temporal analysis of complex dynamical systems. This method is capable of detecting alterations appearing in the characters of the deterministic dynamical systems and provides a simple tool for the real-time analysis of time series data obtained from a complex dynamical system even with black box functionality. The study presents a possible application of the method in the dynamical transition analysis of real EEG records from epilepsy patients. | en_US |
dc.description.sponsorship | Neurology Department | en_US |
dc.description.sponsorship | We thank Prof. Dr. Cemal Ozcan and staff of Neurology Department for their support to our biomedical signal processing studies with EEG data. We also thank Dr. M. Emin Tagluk for fruitful conversations. | en_US |
dc.identifier.doi | 10.1007/s10867-011-9248-2 | |
dc.identifier.endpage | 303 | en_US |
dc.identifier.issn | 0092-0606 | |
dc.identifier.issue | 2 | en_US |
dc.identifier.pmid | 23449743 | en_US |
dc.identifier.scopus | 2-s2.0-84863858688 | en_US |
dc.identifier.scopusquality | N/A | en_US |
dc.identifier.startpage | 293 | en_US |
dc.identifier.uri | https://doi.org/10.1007/s10867-011-9248-2 | |
dc.identifier.uri | https://hdl.handle.net/11616/95722 | |
dc.identifier.volume | 38 | en_US |
dc.identifier.wos | WOS:000302868400008 | en_US |
dc.identifier.wosquality | Q4 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.indekslendigikaynak | PubMed | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Journal of Biological Physics | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Dynamical system analysis | en_US |
dc.subject | Chaos | en_US |
dc.subject | Epilepsy seizure prediction from EEG signal | en_US |
dc.title | A distance-based dynamical transition analysis of time series signals and application to biological systems | en_US |
dc.type | Article | en_US |