A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem.
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 th...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 9; pp. 1003 - 1014 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2011
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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=104672786&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104672786 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2011 vid: 49 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104672786 NLM21472435 2011248235 10.1007/s11517-011-0757-8 NLM21472435 104672786 ppf: 1003 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem. aug: au: Aydin U Dogrusoz YS Aydin, Umit Dogrusoz, Yesim Serinagaoglu affil: Department of Electrical and Electronics Engineering, Middle East Technical University, 06531 Ankara, Turkey sug: subj: Electrocardiography Methods Models, Biological Signal Processing, Computer Assisted Algorithms Animals Dogs Electricity Heart Function Tests ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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