Reducing Error in ECG Forward Simulations With Improved Source Sampling

Tate, Jess; Gillette, Karli; Burton, Brett; Good, Wilson W.; Zenger, Brian; Coll‐Font, Jaume; Brooks, Dana H.; MacLeod, Rob S.

doi:10.3389/fphys.2018.01304

Cited by 13 publications

(16 citation statements)

References 34 publications

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“…These data demonstrated that noise increased ED and decreased correlation, but the differences between conditions were maintained, indicating that whereas noise has an important impact on the activation maps and ED, our observations about different geometries and pacing rate are maintained (Section S3 in Supplementary Material; Figures S4–S7, and Tables S1, S2). Also, the results obtained in this study are of the same order of magnitude observed in previous studies (Wang et al, 2010; Bear et al, 2018b);(Tate et al, 2018).…”

Section: Discussionsupporting

confidence: 91%

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

et al. 2019

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Background: Non-invasive cardiac mapping—also known as Electrocardiographic imaging (ECGi)—is a novel, painless and relatively economic method to map the electrical activation and repolarization patterns of the heart, providing a valuable tool for early identification and diagnosis of conduction abnormalities and arrhythmias. Moreover, the ability to obtain information on cardiac electrical activity non-invasively using ECGi provides the potential for a priori information to guide invasive surgical procedures, improving success rates, and reducing procedure time. Previous studies have shown the influence of clinical variables, such as heart rate, heart size, endocardial wall, and body composition on surface electrocardiogram (ECG) measurements. The influence of clinical variables on the ECG variability has provided information on cardiovascular control and its abnormalities in various pathologies. However, the effects of such clinical variables on the Body Surface Potential (BSP) and ECGi maps have yet to be systematically investigated. Methods: In this study we investigated the effects of heart size, intracardiac thickness, and heart rate on BSP and ECGi maps using a previously-developed 3D electrophysiologically-detailed ventricles-torso model. The inverse solution was solved using the three different Tikhonov regularization methods. Results: Through comparison of multiple measures of error/accuracy on the ECGi reconstructions, our results showed that using different heart geometries to solve the forward and inverse problems produced a larger estimated focal excitation location. An increase of ~2 mm in the Euclidean distance error was observed for an increase in the heart size. However, the estimation of the location of focal activity was still able to be obtained. Similarly, a Euclidean distance increase was observed when the order of regularization was reduced. For the case of activation maps reconstructed at the same ectopic focus location but different heart rates, an increase in the errors and Euclidean distance was observed when the heart rate was increased. Conclusions: Non-invasive cardiac mapping can still provide useful information about cardiac activation patterns for the cases when a different geometry is used for the inverse problem compared to the one used for the forward solution; rapid pacing rates can induce order-dependent errors in the accuracy of reconstruction.

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Section: Discussionsupporting

confidence: 91%

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

et al. 2019

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“…The error in the forward simulation was also manifested through changes in extrema location in the body surface potential maps (Figure 2). Similar behavior was demonstrated in previous studies analyzing the effect of source sampling error on forward simulations [5,7]. Interestingly, the error generated from reduced cardiac sampling is easier to observe by analyzing temporal signals than body surface potential maps, despite the error originating from spatial processes.…”

Section: Discussionsupporting

confidence: 84%

“…Electrocardiographic Imaging (ECGI) non-invasively describes the electrical activity on the surface of the heart and is increasingly being used to diagnosis and treatment of cardiac arrhythmias. ECGI relies heavily on ECG forward models, yet recent research has shown these models to produce more error than commonly thought [1] and that properly sampled cardiac sources can play a role in reducing error [7]. Despite the risk of increased error, ECG forward models are often subsampled to reduce computational cost.…”

Section: Introductionmentioning

confidence: 99%

Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations

Tate

Schuler

Dössel

et al. 2019

Functional Imaging and Modeling of the Heart

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Electrocardiographic Imaging (ECGI) requires robust ECG forward simulations to accurately calculate cardiac activity. However, many questions remain regarding ECG forward simulations, for instance: there are not common guidelines for the required cardiac source sampling. In this study we test equivalent double layer (EDL) forward simulations with differing cardiac source resolutions and different spatial interpolation techniques. The goal is to reduce error caused by undersampling of cardiac sources and provide guidelines to reduce said source undersampling in ECG forward simulations. Using a simulated dataset sampled at 5 spatial resolutions, we computed body surface potentials using an EDL forward simulation pipeline. We tested two spatial interpolation methods to reduce error due to undersampling triangle weighting and triangle splitting. This forward modeling pipeline showed high frequency artifacts in the predicted ECG time signals when the cardiac source resolution was too low. These low resolutions could also cause shifts in extrema location on the body surface maps. However, these errors in predicted potentials can be mitigated by using a spatial interpolation method. Using spatial interpolation can reduce the number of nodes required for accurate body surface potentials from 9,218 to 2,306. Spatial interpolation in this forward model could also help improve accuracy and reduce computational cost in subsequent ECGI applications.

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“…These findings and initial activations points found around or onto papillary muscles or the insertion of the moderator band indicate a strong influence of these electro-anatomical structures on the estimation of sinus rhythm. This is further anticipated due to earlier studies showing the effect of segmentation error and incorporation of the atrial myocardial surface on the iECG procedure [5,6]. Incorporation of the endocardial electro-anatomical structures is expected to similarly affect the robustness of cardiac activation sequences and decrease modelling error.…”

Section: Discussionmentioning

confidence: 90%

“…A correct anatomical representation of the complete cardiac surface is relevant [5,6] to reduce simulation error. Incorporation of these electro-anatomical structures is relevant to improve sinus rhythm simulation in the EDL based iECG method.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Cardiac Source Model Accuracy by Incorporating Endocardial Electro-anatomical Structures

Boonstra¹,

Roudijk²,

Loh³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Non-invasive estimation of the cardiac activation sequence by solving the inverse problem of electrocardiography based on the equivalent double layer is appropriate for premature ventricular beats. However, the method has to be improved to simulate sinus rhythm because the His-Purkinje system is involved. Therefore, a correct anatomical representation of the complete cardiac surface including endocardial electro-anatomical structures may be relevant. Comparing models with and without these electro-anatomical structures, the initial estimation of the cardiac activation sequence was often similar. However, the estimation of initial foci in the extensive model was more similar to initial foci known from in vitro and invasive mapping studies. Therefore, incorporation of electro-anatomical structures may improve robustness of modeling cardiac activation sequences with His-Purkinje involvement.

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Reducing Error in ECG Forward Simulations With Improved Source Sampling

Cited by 13 publications

References 34 publications

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations

Optimizing Cardiac Source Model Accuracy by Incorporating Endocardial Electro-anatomical Structures

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