ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.
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 invers...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 10; pp. 2093 - 2114 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139126439&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139126439 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2019 vid: 57 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139126439 139126439 NLM31363890 10.1007/s11517-019-02018-6 NLM31363890 139126439 ppf: 2093 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging. aug: au: Erenler, Taha Serinagaoglu Dogrusoz, Yesim affil: Department of Electrical and Electronics Engineering, Middle East Technical University, Üniversiteler Mahallesi Dumlupınar Bulvarı No:1, 06800, Çankaya, Ankara, Turkey sug: subj: Algorithms Electrocardiography Image Processing, Computer Assisted Body Surface Potential Mapping Probability Animals Computer Simulation Time Factors Databases Pericardium Sensitivity and Specificity Dogs ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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