Imaging of Bioelectric Sources in the Heart Using a Cellular Automaton Model

Dössel, Olaf; Bauer, Wolfgang R.; Farina, D.; Kaltwasser, C.; Skipa, O.

doi:10.1109/iembs.2005.1616603

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Recent model-based approaches have been implemented and validated in animal (Han et al, 2012; Han et al, 2011; Liu et al, 2008; Zhang et al, 2004; He et al, 2002; Li et al, 2001) and human (Dossel et al, 2005) studies. Using electrical activation models based on artificial neural networks and cellular automata, the results of these studies agreed with measured surface electrocardiograms, activation times, and known source locations within reasonable error bounds.…”

Section: Introductionmentioning

confidence: 99%

Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block

Villongco

Krummen

Stark

et al. 2014

Progress in Biophysics and Molecular Biology

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Patient-specific computational models have promise to improve cardiac disease diagnosis and therapy planning. Here a new method is described to simulate left-bundle branch block (LBBB) and RV-paced ventricular activation patterns in three dimensions from non-invasive, routine clinical measurements. Activation patterns were estimated in three patients using vectorcardiograms (VCG) derived from standard 12-lead electrocardiograms (ECG). Parameters of a monodomain model of biventricular electrophysiology were optimized to minimize differences between the measured and computed VCG. Electroanatomic maps of local activation times measured on the LV and RV endocardial surfaces of the same patients were used to validate the simulated activation patterns. For all patients, the optimal estimated model parameters predicted a time-averaged mean activation dipole orientation within 6.7±0.6° of the derived VCG. The predicted local activation times agreed within 11.5±0.8 ms of the measured electroanatomic maps, on the order of the measurement accuracy.

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Section: Introductionmentioning

confidence: 99%

Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block

Villongco

Krummen

Stark

et al. 2014

Progress in Biophysics and Molecular Biology

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“…Characteristic parameters like duration and amplitude of transmembrane potential or velocity of propagation are optimized for selected tissue classes or regions in the heart to fit simulated data to the measured data. This way the source distribution and its time course of an individual patient can be reconstructed [88]. …”

Section: Resultsmentioning

confidence: 99%

Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives

Huebner¹,

Goernig²,

Schuepbach³

et al. 2010

GMS German Medical Science; 8:Doc27; ISSN 1612-3174

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“…On the other hand, noninvasive methods which serve this purpose by analyzing the body surface ECGs or the intracavitary electrograms have been investigated, including tracing the moving dipole(s) [34]–[36], reconstructing the epicardial or endocardial potentials [7], [13]–[14] and estimating the heart surface activation sequence [37]. Localizing the origin of the ectopic foci by estimating the 3D cardiac activation was previously proposed by our group [21]–[23] and the 3D inverse approaches have also been reported by other colleagues [31], [38]–[39]. We have preliminarily evaluated this approach in animal models [25], [29].…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms

Liu

Eggen

Swingen

et al. 2012

IEEE Trans. Med. Imaging

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The three-dimensional cardiac electrical imaging (3DCEI) technique was previously developed to estimate the initiation site(s) of cardiac activation and activation sequence from the noninvasively measured body surface potential maps (BSPMs). The aim of the present study was to develop and evaluate the capability of 3DCEI in mapping the transmural distribution of extracellular potentials and localizing initiation sites of ventricular activation in an in vivo animal model. A control swine model (n=10) was employed in this study. The heart-torso volume conductor model and the excitable heart model were constructed based on each animal's pre-operative MR images and a priori known physiological knowledge. Body surface potential mapping and intracavitary noncontact mapping (NCM) were simultaneously conducted during acute ventricular pacing. The 3DCEI analysis was then applied on the recorded BSPMs. The estimated initiation sites were compared to the precise pacing sites; as a subset of the mapped transmural potentials by 3DCEI, the electrograms on the left ventricular endocardium were compared to the corresponding output of the NCM system. Over the 16 LV and 48 RV pacing studies, the averaged localization error was 6.1±2.3 mm, and the averaged correlation coefficient between the estimated endocardial electrograms by 3DCEI and from the NCM system was 0.62±0.09. The present results demonstrate that the 3DCEI approach can well localize the sites of initiation of ectopic beats and can obtain physiologically reasonable transmural potentials in an in vivo setting during focal ectopic beats. This study suggests the feasibility of tomographic mapping of 3D ventricular electrograms from the body surface recordings.

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Imaging of Bioelectric Sources in the Heart Using a Cellular Automaton Model

Cited by 6 publications

References 7 publications

Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block

Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block

Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives

Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms

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