The relation of 12 lead ECG to the cardiac anatomy: The normal CineECG.

Abstract Background The interpretation of the 12‑lead ECG is notoriously difficult and requires experts to distinguish normal from abnormal ECG waveforms. ECG waveforms depend on body build and electrode positions, both often different in males and females. To relate the ECG waveforms to cardiac anatomical structures is even more difficult. The novel CineECG algorithm enables a direct projection of the 12‑lead ECG to the cardiac anatomy by computing the mean location of cardiac activity over time. The aim of this study is to investigate the cardiac locations of the CineECG derived from standard 12‑lead ECGs of normal subjects. Methods In this study we used 6525 12‑lead ECG tracings labelled as normal obtained from the certified Physionet PTB XL Diagnostic ECG Database to construct the CineECG. All 12 lead ECGs were analyzed, and then divided by age groups (18–29,30-39,40-49,50-59,60-69,70–100 years) and by gender (male/female). For each ECG, we computed the CineECG within a generic 3D heart/torso model. Based on these CineECG's, the average normal cardiac location and direction for QRS, STpeak, and TpeakTend segments were determined. Results The CineECG direction for the QRS segment showed large variation towards the left free wall, whereas the STT segments were homogeneously directed towards the septal/apical region. The differences in the CineECG location for the QRS, STpeak, and TpeakTend between the age and gender groups were relatively small (maximally 10 mm at end T-wave), although between the gender groups minor differences were found in the 4 chamber direction angles (QRS 4°, STpeak 5°, and TpeakTend 8°) and LAO (QRS 1°, STpeak 13°, and TpeakTend 30°). Conclusion CineECG demonstrated to be a feasible and pragmatic solution for ECG waveform interpretation, relating the ECG directly to the cardiac anatomy. The variations in depolarization and repolarization CineECG were small within this group of normal healthy controls, both in cardiac location as well as in direction. CineECG may enable an easier discrimination between normal and abnormal QRS and T-wave morphologies, reducing the amount of expert training. Further studies are needed to prove whether novel CineECG can significantly contribute to the discrimination of normal versus abnormal ECG tracings

Disease-Specific Electrocardiographic Lead Positioning for Early Detection of Arrhythmogenic Right Ventricular Cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by replacement of cardiomyocytes by fibrofatty tissue which can lead to ventricular arrhythmias, heart failure or sudden cardiac death. Genetic defects in desmosomal proteins, as plakophilin-2 (PKP2), are known to contribute to disease development. Current electrocardiographic (ECG) criteria for ARVC diagnosis only focus on right precordial leads, but sensitivity of current depolarization criteria is limited. This study aimed to identify additional depolarization criteria with most optimal lead configurations for early detection of ARVC in PKP2 pathogenic mutation carriers. In PKP2-positive ARVC patients (n=7), PKP2 pathogenic variant carriers (n=16) and control subjects without structural heart disease (n=9), 67-lead body surface potential maps (BSPM) were obtained. Terminal QRS-integrals were determined and quantitatively compared to controls using departure mapping. Significantly different terminal QRS-integrals were identified in lead 34 (conventional V3), 40 and 41 (conventional V4). To conclude, a clear distinction between ARVC patients, asymptomatic mutation carriers and healthy controls was observed

Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization

International audienceElectrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fullyautomated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed

Segmentation Uncertainty Quantification in Cardiac Propagation Models

A key part of patient-specific cardiac simulations is segmentation, yet the impact of this subjective and errorprone process hasn't been quantified in most simulation pipelines. In this study we quantify the dependence of a cardiac propagation model on from segmentation variability. We used statistical shape modeling and polynomial Chaos (PC) to capture segmentation variability dependence and applied its affects to a propagation model. We evaluated the predicted local activation times (LATs) an body surface potentials (BSPs) from two modeling pipelines: an EIkonal propagation model and a surfacebased fastest route model. The predicted uncertainty due to segmentation shape variability was distributed near the base of the heart and near high amplitude torso potential regions. Our results suggest that modeling pipelines may have to accommodate segmentation errors if regions of interest correspond to high segmentation error. Further, even small errors could proliferate if modeling results are used to to feed further computations, such as ECGI

Modeling the His-Purkinje Effect in Non-invasive Estimation of Endocardial and Epicardial Ventricular Activation

Inverse electrocardiography (iECG) estimates epi- and endocardial electrical activity from body surface potentials maps (BSPM). In individuals at risk for cardiomyopathy, non-invasive estimation of normal ventricular activation may provide valuable information to aid risk stratification to prevent sudden cardiac death. However, multiple simultaneous activation wavefronts initiated by the His-Purkinje system, severely complicate iECG. To improve the estimation of normal ventricular activation, the iECG method should accurately mimic the effect of the His-Purkinje system, which is not taken into account in the previously published multi-focal iECG. Therefore, we introduce the novel multi-wave iECG method and report on its performance. Multi-wave iECG and multi-focal iECG were tested in four patients undergoing invasive electro-anatomical mapping during normal ventricular activation. In each subject, 67-electrode BSPM were recorded and used as input for both iECG methods. The iECG and invasive local activation timing (LAT) maps were compared. Median epicardial inter-map correlation coefficient (CC) between invasive LAT maps and estimated multi-wave iECG versus multi-focal iECG was 0.61 versus 0.31. Endocardial inter-map CC was 0.54 respectively 0.22. Modeling the His-Purkinje system resulted in a physiologically realistic and robust non-invasive estimation of normal ventricular activation, which might enable the early detection of cardiac disease during normal sinus rhythm

The Effect of Segmentation Variability in Forward ECG Simulation

International audienceSegmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI

The Application of Non-invasive Inverse ECG Techniques in Cardiomyopathy

The electrocardiogram (ECG) plays an important role in systematically assessing cardiac electrical function, but the standard 12-lead ECG only provides only a distant view on cardiac electrical activity. Using non-invasive inverse ECG techniques, additional detailed information on cardiac electrical activity can be obtained by linking cardiac electrical activity to anatomy to enable the identification of subtle disease progression in arrhythmogenic cardiomyopathy. Arrhythmogenic cardiomyopathy is characterized by structural and electrical myocardial remodeling and can manifest as a broad range of lethal phenotypes. In arrhythmogenic cardiomyopathy, electrical remodeling can precede structural and functional changes and sudden cardiac death can be the first disease manifestation. This highlights the need for accurate screening and risk-stratification strategies. The first part of this thesis focusses on describing the optimization of a traditional inverse ECG technique to provide non-invasive insight in endocardial and epicardial cardiac electrical activity by combining 67-lead ECG data with patient specific CT/MRI-based anatomical models. To be able to identify early signs of arrhythmogenic cardiomyopathy development, accurate imaging of sinus rhythm is of importance. Therefore, in Chapter 2, we report on our work regarding the optimization of the inverse ECG technique for the estimation of sinus rhythm and report on its performance (Chapter 3) by comparing it to invasive local activation maps. With the incorporation of a subject-specific anatomy-based model of the His-Purkinje system a physiologically realistic and robust estimation of the ventricular activation sequence is obtained. The optimized inverse ECG technique detected local electrophysiological characteristics in the activation sequence in pathogenic variant carriers with and without any clinical signs of disease (Chapter 4). To further optimize the performance of the inverse ECG technique by developing a new method to model myocardial disease in ECG simulation in Chapter 5 to provide a realistic relation between ECG waveforms and underlying activation sequences. As traditional inverse ECG techniques are mathematically complex and computationally demanding, we focus on CineECG, a new method to image key features of the activation sequence that are difficult to reliably obtain from the ECG. We conceptually validated the technique in cases of bundle branch blocks (Chapter 6) and after evaluation, the method was optimized and validated through a simulation study (Chapter 7). As accurate assessment of subtle ECG changes is limited due to inconsistencies in electrode positioning, we focused on the optimization of the 12-lead ECG acquisition by introducing a 3D-camera based method to reduce electrode placement misplacement (Chapter 8). With this new technique, the identification of subtle changes in the QRS complex during arrhythmogenic cardiomyopathy follow-up may be improved. In Chapter 9, we describe how novel AI-based algorithms may aid current clinical practice together with its potential benefits and challenges. With such algorithms, the complex nature of disease progression in arrhythmogenic cardiomyopathy may be further unraveled. To conclude the thesis, the application of techniques presented in this thesis to enhance diagnosis and risk-stratification in arrhythmogenic cardiomyopathy is described (Chapter 10). The techniques are viewed within the context of possible fields of application in current clinical practice

On the Initial Estimate of Repolarization Times for Inverse Reconstruction Using the Equivalent Dipole Layer Source Model

The equivalent dipole layer (EDL) source model for noninvasive reconstruction of cardiac electrical activity applies a nonlinear parameter estimation procedure starting from an initial estimate of activation and repolarization times. In this paper, we compare two methods to determine the initial estimate for repolarization from that of activation: reversely (method 1) and directly (method 2). In a pig experiment, we found lower errors with higher correlation coefficients between measured and reconstructed repolarization times using method 2 as initial estimate for repolarization for both atrially and ventricularly paced beats. This corresponds with the linear positive relation between measured activation and repolarization time and the discordance of QRS complex and T-wave polarity on the body surface potentials in both atrially and ventricularly paced beats, indicating a similar sequence of activation and repolarization. In human data, there is a big difference in reconstructed repolarization pattern when using method 1 or 2 for initial estimate of repolarization. Here, there is a concordance in the majority of leads for a sinus beat and a discordance in the majority of leads for PVCs. We recommend using the relation of the QRS complex and T-wave to determine which method for initial estimate for the EDL method of the inverse solution is most suitable for each individual beat

Inter-operator segmentation variability induces high premature ventricular contractions localization uncertainty at the heart base

International audienceBackgroundElectrocardiographic imaging (ECGI) is a promising tool for the treatment and diagnosis of cardiac arrhythmias. ECGI estimates non-invasively the electrical activity of the heart using body surface potentials (BSPs) obtained at the body surface in combination with a specific CT/MRI based anatomical models and defined electrode positions. In order to solve the ECGI inverse problem the first step to be considered is indeed the image segmentation and mesh generation.ObjectiveOur main purpose is to evaluate the effect of the inter-operator segmentation variability on the PVC localization.MethodsEight different cardiac segmentations from the same single subject CT-scans were performed by researchers within the consortium for Electrocardiographic Imaging. For all generated meshes, eight ventricular stimulation protocols were used; left and right ventricular free walls (LV, RV), apex, left and right ventricular outflow tract (LVOT, RVOT), septum, and two locations at the left and right heart base (LVB, RVB). BSPs were generated using computational models. We designed two test cases: with and without segmentation uncertainty. In test A, no segmentation uncertainty is considered. In test B, we solve the inverse problem for the eight geometries starting from one single BSP generated with a reference heart geometry. For each test case and for each stimulation protocol we computed the inverse solution using the Method of Fundamental Solutions and assessed the Localization Error (LE) of the pacing sites. In order to quantify the effect of segmentation uncertainty we also computed the difference between LEs obtained in tests B and A.ResultsIn test A, the mean LEs for LV, RV, apex, LVOT, RVOT, septum, LVB and RVB pacings are 7, 7, 5, 12, 14, 18, 13, 15 mm, respectively. In test B, the mean LEs are 7, 7, 5, 17, 23, 17, 16, 23 mm, respectively. The average differences between LEs are 0, 0, -1,5, 8, -1, 3, 8 mm, respectively.ConclusionThis study shows that the effect of the segmentation uncertainty on the localization of PVC is more important for RVOT, LVOT, RVB and LVB. We believe that the high uncertainty is due to the variability of segmentations at the base of the heart. These findings suggest that uncertainty in cardiac segmentation can have a significant impact on ECGI and its interpretability in clinical applications; therefore, careful segmentation is strongly recommended, especially at the base of the heart