Noninvasive Electrocardiographic Imaging (ECGI): Application of the Generalized Minimal Residual (GMRes) Method

Ramanathan, Charulatha; Jia, Ping; Ghanem, Raja N.; Calvetti, Daniela; Rudy, Yoram

doi:10.1114/1.1588655

Cited by 84 publications

(60 citation statements)

References 21 publications

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“…Epicardial potentials are then computed from the body-surface potentials applying a constraint-based Tikhonov regularization (25) and͞or an iterative GMRes method (26). Details of the mathematical methods have been provided in previous publications (25,(27)(28)(29).…”

Section: Methodsmentioning

confidence: 99%

Activation and repolarization of the normal human heart under complete physiological conditions

Ramanathan

Jia

Ghanem

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

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Knowledge of normal human cardiac excitation stems from isolated heart or intraoperative mapping studies under nonphysiological conditions. Here, we use a noninvasive imaging modality (electrocardiographic imaging) to study normal activation and repolarization in intact unanesthetized healthy adults under complete physiological conditions. Epicardial potentials, electrograms, and isochrones were noninvasively reconstructed. The normal electrophysiological sequence during activation and repolarization was imaged in seven healthy subjects (four males and three females). Electrocardiographic imaging depicted salient features of normal ventricular activation, including timing and location of the earliest right ventricular (RV) epicardial breakthrough in the anterior paraseptal region, subsequent RV and left ventricular (LV) breakthroughs, apex-to-base activation of posterior LV, and late activation of LV base or RV outflow tract. The repolarization sequence was unaffected by the activation sequence, supporting the hypothesis that in normal hearts, local action potential duration (APD) determines local repolarization time. Mean activation recovery interval (ARI), reflecting local APD, was in the typical human APD range (235 ms). Mean LV apex-to-base ARI dispersion was 42 ms. Average LV ARI exceeded RV ARI by 32 ms. Atrial images showed activation spreading from the sinus node to the rest of the atria, ending at the left atrial appendage. This study provides previously undescribed characterization of human cardiac activation and repolarization under normal physiological conditions. A common sequence of activation was identified, with interindividual differences in specific patterns. The repolarization sequence was determined by local repolarization properties rather than by the activation sequence, and significant dispersion of repolarization was observed between RV and LV and from apex to base.noninvasive electrocardiographic imaging ͉ normal cardiac activation and repolarization ͉ normal sinus rhythm U nderstanding normal cardiac excitation provides a necessary baseline for understanding abnormal cardiac electrical activity and rhythm disorders of the heart, a major cause of death and disability. So far, knowledge of normal human cardiac excitation has been obtained mostly through extrapolation from animal studies, including canine (1, 2) and chimpanzee (3). In addition, human data have been obtained from intraoperative epicardial mapping (4-7) and isolated human hearts (8). Extrapolation of animal studies to humans is limited by interspecies differences in anatomy and electrophysiology. Also, the animal and human studies were conducted under nonphysiological conditions (e.g., anesthesia effects and heart exposure during intraoperative mapping; effects of isolation and absence of neural inputs, mechanical loading, and normal perfusion in isolated heart studies). Until now, it has not been possible to study cardiac excitation in intact healthy subjects under normal physiological conditions because of the unavailabili...

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Section: Methodsmentioning

confidence: 99%

Activation and repolarization of the normal human heart under complete physiological conditions

Ramanathan

Jia

Ghanem

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

229

166

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“…The Tikhonov regularization method 76 with CRESO-determined regularization parameter 24 is used to stabilize the inverse procedure and obtain , similar to our previous ECGI inverse computations using mesh-based BEM. 15,16,32,33,47,56,61,62,[65][66][67][68][69][70][71] Once the coefficient vector is obtained, u(x) can be computed at any location in the domain using: (5) The epicardial potential can then be calculated using: (6) Epicardial potentials are calculated using (6) on many epicardial nodes; numbers are provided for each dataset in the Results section. An epicardial potential map, reflecting the spatial distribution of potentials on the epicardial surface, is computed every millisecond during the cardiac cycle.…”

Section: Formulating the Methods Of Fundamental Solutions For Ecgimentioning

confidence: 99%

Application of the Method of Fundamental Solutions to Potential-based Inverse Electrocardiography

2006

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Potential-based inverse electrocardiography is a method for the noninvasive computation of epicardial potentials from measured body surface electrocardiographic data. From the computed epicardial potentials, epicardial electrograms and isochrones (activation sequences), as well as repolarization patterns can be constructed. We term this noninvasive procedure Electrocardiographic Imaging (ECGI). The method of choice for computing epicardial potentials has been the Boundary Element Method (BEM) which requires meshing the heart and torso surfaces and optimizing the mesh, a very time-consuming operation that requires manual editing. Moreover, it can introduce mesh-related artifacts in the reconstructed epicardial images. Here we introduce the application of a meshless method, the Method of Fundamental Solutions (MFS) to ECGI. This new approach that does not require meshing is evaluated on data from animal experiments and human studies, and compared to BEM. Results demonstrate similar accuracy, with the following advantages: 1. Elimination of meshing and manual mesh optimization processes, thereby enhancing automation and speeding the ECGI procedure. 2. Elimination of mesh-induced artifacts. 3. Elimination of complex singular integrals that must be carefully computed in BEM. 4. Simpler implementation. These properties of MFS enhance the practical application of ECGI as a clinical diagnostic tool.

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“…In Figure 9 is represented the mean solution as a transmural activation map (9a) that was compared with the CardioInsight epicardial inverse solution [31] (9b). The CardioInsight solution is interesting for comparison even if it is only an epicardial surface reconstruction.…”

Section: B Estimated Sinus Rhythm Activation Mapsmentioning

confidence: 99%

Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy

Giffard‐Roisin

Delingette²,

Jackson

et al. 2019

IEEE Trans. Biomed. Eng.

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Abstract-Goal: Non-invasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. Methods: We propose in this manuscript an approach to tackle these two issues. First, we analyse more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalised predictions online. Results: We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted 5 different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. Conclusion: We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from non-invasive measurements for fast patient-specific predictions. Significance: The fast CRT pacing predictions are a step forward to a noninvasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.

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Noninvasive Electrocardiographic Imaging (ECGI): Application of the Generalized Minimal Residual (GMRes) Method

Cited by 84 publications

References 21 publications

Activation and repolarization of the normal human heart under complete physiological conditions

Activation and repolarization of the normal human heart under complete physiological conditions

Application of the Method of Fundamental Solutions to Potential-based Inverse Electrocardiography

Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy

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