A possible mechanism for electrocardiographically silent changes in cardiac repolarization

2011

Med Biol Eng Comput

In this article, we aimed to reduce the effects of geometric errors and measurement noise on the inverse problem of Electrocardiography (ECG) solutions. We used the Kalman filter to solve the inverse problem in terms of epicardial potential distributions. The geometric errors were introduced into the problem via wrong determination of the size and location of the heart in simulations. An error model, which is called the enhanced error model (EEM), was modified to be used in inverse problem of ECG to compensate for the geometric errors. In this model, the geometric errors are modeled as additive Gaussian noise and their noise variance is added to the measurement noise variance. The Kalman filter method includes a process noise component, whose variance should also be estimated along with the measurement noise. To estimate these two noise variances, two different algorithms were used: (1) an algorithm based on residuals, (2) expectation maximization algorithm. The results showed that it is important to use the correct noise variances to obtain accurate results. The geometric errors, if ignored in the inverse solution procedure, yielded incorrect epicardial potential distributions. However, even with a noise model as simple as the EEM, the solutions could be significantly improved.

Section: Resultsmentioning

confidence: 99%

A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem

Aydın

2011

Med Biol Eng Comput

“…MacLeod and his co-workers at the University of Utah Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI) [16]. The epicardial measurements are taken from 490 points; sampling rate is 1000 Hz.…”

Section: Resultsmentioning

confidence: 99%

Use of genetic algorithm for selection of regularization parameters in multiple constraint inverse ECG problem

Gavgani

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

2011

Tikhonov regularization is one of the most widely used regularization approaches in literature to overcome the ill-posedness of the inverse electrocardiography problem. However, the resulting solutions are biased towards the constraint used for regularization. One alternative to obtain improved results is to employ multiple constraints in the cost function. This approach has been shown to produce better results; however finding appropriate regularization parameters is a serious limitation of the method. In this study, we propose estimating multiple regularization parameters using a genetic algorithm based approach. Applicability of the approach is demonstrated here using two and three constraints. The results show that GA based multiple constraints approach improves the Tikhonov regularization solutions.

“…The simulations are carried out using the epicardial potential measurements obtained from a dog heart by Robert S. Macleod and his team at University of Utah Nora Eccles Harrison Cardiovascular Research and Training Institute [15]. In these simulations, epicardial potential measurements from 490 points are used and the torso potentials are simulated at 771 points.…”

Section: Resultsmentioning

confidence: 99%

Imaging the electrical activity of the heart using a Kalman filter based approach: Comparison of results using different STM's

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Aydın

2009

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 results indicate that the Kalman filter that uses a STM obtained from Bayes-MAP solutions produces accurate solutions.