Estimation of state transition matrix in the Kalman filter based inverse ECG solution with the help of training sets Ters EKG probleminin Kalman filtre ile çözümünde durum geçiş matrisinin eǧitici kümeler yardimi ile kestirimi

Date

2009-10-27

Author

Aydin, Ümit
Serinağaoğlu Doğrusöz, Yeşim

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

223
views

0
downloads

At this study the main motivation is to solve inverse problem of ECG with Kalman filter. In order to obtain feasible solutions determination of the state transition matrix (STM) correctly is vital. In literature the STM is usually found by using the test data itself which is not a realistic scenario. The major goal of this study is to determine STM without using test data. For that purpose a two stage method is suggested. At the first step the probability density function (pdf) is calculated using training sets and then this pdf is used to find Bayes-MAP solution which uses only spatial information. At the second step, the Bayes-MAP solution is used to find STM and later on, that STM is used in Kalman filter to obtain final results. It is seen that the results obtained with this method are better then normal Bayes-MAP results and the errors are within acceptable limits. So it is concluded that the usage of Bayes-MAP solutions in STM determination is a serious alternative for STM estimation

Subject Keywords

Inverse problems, Solid modeling, Testing, Kalman filters, Electrocardiography, State estimation

URI

https://hdl.handle.net/11511/38326

DOI

https://doi.org/10.1109/biyomut.2009.5130254

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Use of Activation Time Based Kalman Filtering in Inverse Problem of Electrocardiography Aydin, Umit; Serinağaoğlu Doğrusöz, Yeşim (2008-11-27) The goal of this study is to solve inverse problem of electrocardiography (ECG) in terms of epicardial potentials using body surface (torso) potential measurements. The problem is ill-posed and regularization must be applied. Kalman filter is one of the regularization approaches, which includes both spatial and temporal correlations of epicardial potentials. However, in order to use the Kalman filter, one needs the state transition matrix (STM) that models the time evolution of the epicardial potentials. In...
Comparison of Kalman filter and Bayesian-MAP approaches in the spatio-temporal solution of the inverse electrocardiography Ters elektrokardiyografinin zaman-uzamsal çözümünde Kalman filtre ve Bayes-MAP yöntemlerinin karşilaştirilmasi Aydin, Ümit; Serinağaoğlu Doğrusöz, Yeşim (2010-07-15) In this study some of the spatial and spatio-temporal methods for the solution of the inverse problem of electrocardiography (ECG) are compared with each other. Comparisons are also made for the cases with geometric errors, where the location of the heart is shifted for 10mm and the size of the heart is reduced by 5%. The compared methods are the Kalman filter and Bayesian maximum a posteriori estimation (MAP). Two different Bayesian-MAP algorithms are used. While one uses only spatial information the other...
IMAGING THE ELECTRICAL ACTIVITY OF THE HEART USING A KALMAN FILTER BASED APPROACH: COMPARISON OF RESULTS USING DIFFERENT STM'S Serinağaoğlu Doğrusöz, Yeşim (2009-07-01) Kalman filter based solutions have been of particular interest in inverse problem of Electrocardiography (ECG) in recent years. One of the major problems with this approach however is the determination of the state transition matrix (STM) that relates the epicardial potentials at the current time instant to the potentials at the previous time instant. In this work, we use the solutions of Tikhonov regularization and Bayes-MAP algorithm to construct a STM, and we use these STMs in Kalman filtering. Our resul...
Comparison of Bayesian MAP Estimation and Kalman Filter Methods in the Solution of Spatio-Temporal Inverse ECG Problem Aydin, Umit; Serinağaoğlu Doğrusöz, Yeşim (2009-09-12) In this study, spatial only, and spatio-temporal Bayesian Maximum a Posteriori (MAP) methods and an another spatio-temporal method, the Kalman filter approach, are used to solve the inverse electrocardiography (ECG) problem. Training sets are used to obtain the required a priori information for all methods. Two different approaches are employed to calculate the state transition matrix (STM), which maps the epicardial potentials in two consecutive time instants in the Kalman filter method. The first one uses...
Improved performance of Bayesian solutions for inverse Electrocardiography using multiple information sources Serinağaoğlu Doğrusöz, Yeşim; MACLEOD, Robert (2006-10-01) The usual goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface potentials and a mathematical model that relates the sources to the measurements. Due to attenuation and smoothing that occurs in the thorax, the inverse ECG problem is ill-posed and imposition of a priori constraints is needed to combat this ill-posedness. When the problem is posed in terms of reconstructing heart surface potentials, solutions have not yet achieved clinical utility; limitation...

Citation Formats

Ü. Aydin and Y. Serinağaoğlu Doğrusöz, “Estimation of state transition matrix in the Kalman filter based inverse ECG solution with the help of training sets Ters EKG probleminin Kalman filtre ile çözümünde durum geçiş matrisinin eǧitici kümeler yardimi ile kestirimi,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38326.