Improved performance of Bayesian solutions for inverse Electrocardiography using multiple information sources

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; limitations include the limited availability of good a priori information about the solution and the lack of a "good" error metric. We describe an approach that combines body surface measurements and standard forward models with two additional information sources: statistical prior information about epicardial potential distributions and sparse simultaneous measurements of epicardial potentials made with multielectrode coronary venous catheters. We employ a Bayesian methodology which offers a general way to incorporate these information sources and additionally provides statistical performance analysis tools. In a simulation study, we first compare solutions using one or more of these information sources. Then, we study the effects of varying the number of sparse epicardial potential measurements on reconstruction accuracy. To evaluate accuracy, we used the Bayesian error covariance as well as traditional error metrics such as relative error. Our results show that including even sparsely sampled information from coronary venous catheters can substantially improve the reconstruction of epicardial potential distributions and that a Bayesian framework provides a feasible approach to using this information. Moreover, computing the Bayesian error standard deviations offers a means to indicate confidence in the results even in the absence of validation data.


Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem
ONAK, Onder Nazim; Serinağaoğlu Doğrusöz, Yeşim; WEBER, GERHARD WİLHELM (2018-01-01)
The goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface measurements and a mathematical model of torso-heart geometry that relates the sources to the measurements. This problem is ill-posed due to attenuation and smoothing that occur inside the thorax, and small errors in the measurements yield large reconstruction errors. To overcome this, ill-posedness, traditional regularization methods such as Tikhonov regularization and truncated singular value decom...
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...
Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints
Serinağaoğlu Doğrusöz, Yeşim; Gavgani, Alireza Mazloumi (2013-04-01)
In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach proposed in Brooks et al. (IEEE Trans Biomed Eng 46(1):3-18, 1999) and extends its practical applicability to include more than two constraints by finding appropriate values for the multiple regularization parameter...
Comparison of Dictionary-Based Image Reconstruction Algorithms for Inverse Problems
Dogan, Didem; Öktem, Sevinç Figen (2020-10-07)
Many inverse problems in imaging involve measurements that are in the form of convolutions. Sparsity priors are widely exploited in their solutions for regularization as these problems are generally ill-posed. In this work, we develop image reconstruction methods for these inverse problems using patchbased and convolutional sparse models. The resulting regularized inverse problems are solved via the alternating direction method of multipliers (ADMM). The performance of the developed algorithms is investigat...
Evaluation of multivariate adaptive non-parametric reduced-order model for solving the inverse electrocardiography problem: a simulation study
Onak, Onder Nazim; Serinağaoğlu Doğrusöz, Yeşim; Weber, Gerhard Wilhelm (Springer Science and Business Media LLC, 2019-05-01)
In the inverse electrocardiography (ECG) problem, the goal is to reconstruct the heart's electrical activity from multichannel body surface potentials and a mathematical model of the torso. Over the years, researchers have employed various approaches to solve this ill-posed problem including regularization, optimization, and statistical estimation. It is still a topic of interest especially for researchers and clinicians whose goal is to adopt this technique in clinical applications. Among the wide range of...
Citation Formats
Y. Serinağaoğlu Doğrusöz and R. MACLEOD, “Improved performance of Bayesian solutions for inverse Electrocardiography using multiple information sources,” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, pp. 2024–2034, 2006, Accessed: 00, 2020. [Online]. Available: