当前位置： SCI文献检索 > MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING期刊下所有文献 > ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

Abstract：

:In electrocardiographic imaging (ECGI), one solves the inverse problem of electrocardiography (ECG) to reconstruct equivalent cardiac sources based on the body surface potential measurements and a mathematical model of the torso. Due to attenuation and spatial smoothing within the torso, this inverse problem is ill-posed. Among many regularization approaches used in the ECG literature to overcome this ill-posedness, statistical techniques have received great attention because of their flexibility to represent the data, and ability to provide performance evaluation tools for quantification of uncertainties and errors in the model. However, despite their potential to accurately reconstruct the equivalent cardiac sources, one major challenge in these methods is how to best utilize the prior information available in terms of training data. In this paper, we address the question of how to define the prior probability distributions (pdf) of the sources and the error terms so that we can obtain more accurate and robust inverse solutions. We employ two methods, maximum likelihood (ML) and maximum a posteriori (MAP), for estimating the model parameters such as the prior pdfs, error pdfs, and the state-transition matrix, based on the same training data. These model parameters are then used for the state-space representation and estimation of the epicardial potentials, which constitute the equivalent cardiac sources in this study. The performances of ML- and MAP-based model parameter estimation methods are evaluated qualitatively and quantitatively at various noise levels and geometric disturbances using two different simulated datasets. Bayesian MAP estimation, which is also a well-known statistical inversion technique, and Tikhonov regularization, which can be formulated as a special and simplified version of Bayesian MAP estimation, have been included here for comparison with the Kalman filtering method. Our results show that the state-space approach outperforms Bayesian MAP estimation in all cases; ML yields accurate results when the test and training beats come from the same physiological model, but MAP is superior to ML, especially if the test and training beats are from different physiological models. Graphical Abstract ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

journal_name

Med Biol Eng Comput

journal_title

Medical & biological engineering & computing

authors

Erenler T,Serinagaoglu Dogrusoz Y

doi

10.1007/s11517-019-02018-6

subject

Has Abstract

pub_date

2019-10-01 00:00:00

pages

2093-2113

issue

eissn

0140-0118

issn

1741-0444

pii

10.1007/s11517-019-02018-6

journal_volume

pub_type

杂志文章

在线工具

ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.