Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Comparison of ML and MAP parameter estimation techniques for the solution of inverse electrocardiography problem
Download
index.pdf
Date
2018
Author
Erenler, Taha
Metadata
Show full item record
Item Usage Stats
94
views
41
downloads
Cite This
This study aims to determine the cardiac electrical activity from body surface potential measurements. This problem is called the inverse problem of electrocardiography. Reconstruction of the cardiac electrical activity from the body surface potential measurements is not an easy task, since this problem has an ill-posed nature due to attenuation and spatial smoothing inside the medium between the source and the measurement sites, meaning that even small errors in the mathematical model or noise in the measurements may yield unbounded errors or large oscillations in the solutions. One remedy for this ill-posedness is to apply regularization, where one imposes deterministic or statistical constraints on the solution based on available a priori information. In this thesis, Tikhonov regularization, Bayesian maximum a posteriori estimation (BMAP), Kalman filter and regularized Kalman filter approaches are used to solve the inverse problem of electrocardiography. In the context of Kalman filter, maximum likelihood (ML) and maximum a posteriori (MAP) estimation are used to find Kalman filter parameters. By estimating Kalman filter parameters, we aim to find an answer to an open question of how the essential parameters in the state-space representation are found without claiming strong assumptions in the literature. The results showed that the mean correlation coefficient ranges from 0.99 to 0.66 for MLIF and from 0.97 to 0.72 for MAPIF under 30 dB measurement noise. Our study showed that ML estimation works well when the training set data and test data are similar. However, due to over-fitting nature of the ML estimation, MAP estimation should be preferred in order to improve generalizability of the method.
Subject Keywords
Electrocardiography.
,
Kalman filtering.
URI
http://etd.lib.metu.edu.tr/upload/12622779/index.pdf
https://hdl.handle.net/11511/27672
Collections
Graduate School of Natural and Applied Sciences, Thesis
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...
Use of genetic algorithm for selection of regularization parameters in multiple constraint inverse ECG problem
Mazloumi Gavgani, Alireza; Serinağaoğlu Doğrusöz, Yeşim; Department of Electrical and Electronics Engineering (2011)
The main goal in inverse and forward problems of electrocardiography (ECG) is to better understand the electrical activity of the heart. In the forward problem of ECG, one obtains the body surface potential (BSP) distribution (i.e., the measurements) when the electrical sources in the heart are assumed to be known. The result is a mathematical model that relates the sources to the measurements. In the inverse problem of ECG, the unknown cardiac electrical sources are estimated from the BSP measurements and ...
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...
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
Aydin, Ümit; Serinağaoğlu Doğrusöz, Yeşim (2009-10-27)
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 ...
Critical Fluid Velocities for Removing Cuttings Bed Inside Horizontal and Deviated Wells
Ozbayoglu, M. E.; Saasen, A.; Sorgun, M.; Svanes, K. (2010-01-01)
This study aims to estimate the critical fluid flow velocity for preventing the development of a stationary bed using empirical correlations valid for horizontal and highly inclined wellbores that can be easily used at the field. For this purpose, experiments have been conducted at METU-PETE Cuttings Transport Flow Loop for various conditions. Observations showed that a stationary bed is developed when the fuid velocity is less than 6.0 ft/s, and a critical fluid velocity of 8.0 ft/s is required to establis...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
T. Erenler, “Comparison of ML and MAP parameter estimation techniques for the solution of inverse electrocardiography problem,” M.S. - Master of Science, Middle East Technical University, 2018.