DSpace Repository

Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother

Show simple item record

dc.contributor.author Aslan, Serdar
dc.contributor.author Cemgil, Ali Taylan
dc.contributor.author Aslan, Murat Samil
dc.contributor.author Töreyin, Behçet Uğur
dc.contributor.author Akın, Ata
dc.date.accessioned 2020-04-16T21:21:26Z
dc.date.available 2020-04-16T21:21:26Z
dc.date.issued 2016-02
dc.identifier.citation Aslan, Serdar...et al., "Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother", Biomedical Signal Processing and Control, Vol. 24, pp. 47-62, (2016). tr_TR
dc.identifier.issn 1746-8094
dc.identifier.issn 1746-8108
dc.identifier.uri http://hdl.handle.net/20.500.12416/3249
dc.description.abstract The joint estimation of the parameters and the states of the hemodynamic model from the blood oxygen level dependent (BOLD) signal is a challenging problem. In the functional magnetic resonance imaging (fMRI) literature, quite interestingly, many proposed algorithms work only as a filtering method. This makes the estimation of hidden states and parameters less reliable compared with the algorithms that use smoothing. In standard implementations, smoothing is performed only once. However, joint state and parameter estimation can be improved substantially by iterating smoothing schemes such as the extended Kalman smoother (IEKS). In the fMRI literature, extended Kalman filtering is thought to be less accurate than standard particle filtering (PF). We compared EKF with PF and observed that the contrary is true. We improved the EKF performance by adding smoother. By iterative scheme joint hemodynamic and parameter estimation is improved substantially. We compared IEKS performance with the square-root cubature Kalman smoother (SCKS) algorithm. We show that its accuracy for the state and the parameter estimation is better and much faster than iterative SCKS. SCKS was found to be a better estimator than the dynamic expectation maximization (DEM), EKF, local linearization filter (LLF) and PP methods. We show in this paper that IEKS is a better estimator than iterative SCKS under different process and measurement noise conditions. As a result, IEKS seems to be the best method we evaluated in all aspects. (C) 2015 Elsevier Ltd. All rights reserved. tr_TR
dc.language.iso eng tr_TR
dc.publisher Elsevier SCI LTD tr_TR
dc.relation.isversionof 10.1016/j.bspc.2015.09.006 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Hemodynamic Model tr_TR
dc.subject Extented Kalman Filter/Smoother tr_TR
dc.subject Cubature Kalman Filter/Smoother tr_TR
dc.title Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother tr_TR
dc.type article tr_TR
dc.relation.journal Biomedical Signal Processing and Control tr_TR
dc.contributor.authorID 19325 tr_TR
dc.identifier.volume 24 tr_TR
dc.identifier.startpage 47 tr_TR
dc.identifier.endpage 62 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record