ISE/ISHNE expert consensus statement on the ECG diagnosis of left ventricular hypertrophy: The change of the paradigm

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria. The classical paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces, reflected in the augmented QRS amplitude. However, the low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm. The theoretical background for voltage measured at the body surface is defined by the solid angle theorem, which relates the measured voltage to spatial and non-spatial determinants. The spatial determinants are represented by the extent of the activation front and the distance of the recording electrodes. The non-spatial determinants comprise electrical characteristics of the myocardium, which are comparatively neglected in the interpretation of the QRS patterns. Various clinical conditions are associated with LVH. These conditions produce considerable diversity of electrical properties alterations thereby modifying the resultant QRS patterns. The spectrum of QRS patterns observed in LVH patients is quite broad, including also left axis deviation, left anterior fascicular block, incomplete and complete left bundle branch blocks, Q waves, and fragmented QRS. Importantly, the QRS complex can be within normal limits. The new paradigm stresses the electrophysiological background in interpreting QRS changes, i.e., the effect of the non-spatial determinants. This postulates that the role of ECG is not to estimate LV size in LVH, but to understand and decode the underlying electrical processes, which are crucial in relation to cardiovascular risk assessment.

ISE/ISHNE Expert Consensus Statement on ECG Diagnosis of Left Ventricular Hypertrophy: The Change of the Paradigm. The joint paper of the International Society of Electrocardiology and the International Society for Holter Monitoring and Noninvasive Electrocardiology

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria, i.e. the increased QRS complex amplitude in defined leads. The classical ECG diagnostic paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces. These increased forces are reflected in the augmented QRS amplitude in the corresponding leads. However, the clinical observations document increased QRS amplitude only in the minority of patients with LVH. The low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm.

A two-step inverse solution for a single dipole cardiac source

Introduction: The inverse problem of electrocardiography noninvasively localizes the origin of undesired cardiac activity, such as a premature ventricular contraction (PVC), from potential recordings from multiple torso electrodes. However, the optimal number and placement of electrodes for an accurate solution of the inverse problem remain undetermined. This study presents a two-step inverse solution for a single dipole cardiac source, which investigates the significance of the torso electrodes on a patient-specific level. Furthermore, the impact of the significant electrodes on the accuracy of the inverse solution is studied. Methods: Body surface potential recordings from 128 electrodes of 13 patients with PVCs and their corresponding homogeneous and inhomogeneous torso models were used. The inverse problem using a single dipole was solved in two steps: First, using information from all electrodes, and second, using a subset of electrodes sorted in descending order according to their significance estimated by a greedy algorithm. The significance of electrodes was computed for three criteria derived from the singular values of the transfer matrix that correspond to the inversely estimated origin of the PVC computed in the first step. The localization error (LE) was computed as the Euclidean distance between the ground truth and the inversely estimated origin of the PVC. The LE obtained using the 32 and 64 most significant electrodes was compared to the LE obtained when all 128 electrodes were used for the inverse solution. Results: The average LE calculated for both torso models and using all 128 electrodes was 28.8 ± 11.9 mm. For the three tested criteria, the average LEs were 32.6 ± 19.9 mm, 29.6 ± 14.7 mm, and 28.8 ± 14.5 mm when 32 electrodes were used. When 64 electrodes were used, the average LEs were 30.1 ± 16.8 mm, 29.4 ± 12.0 mm, and 29.5 ± 12.6 mm. Conclusion: The study found inter-patient variability in the significance of torso electrodes and demonstrated that an accurate localization by the inverse solution with a single dipole could be achieved using a carefully selected reduced number of electrodes.

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0 mm for the dipole-based model and 28.9-39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

The Effect of Segmentation Variability in Forward ECG Simulation

Segmentation 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.

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

Electrocardiographic 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 fully-automated 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.

The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions

Goal:

To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data.

Methods:

Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded.

Results:

HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05).

Conclusion:

BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.

Reduction of Effects of Noise on the Inverse Problem of Electrocardiography with Bayesian Estimation

To overcome the ill-posed nature of the inverse problem of electrocardiography (ECG) and stabilize the solutions, regularization is used. Despite several studies on noise, effect of prefiltering of ECG signals on the regularized inverse solutions has not been explored. We used Bayesian estimation for solving the inverse ECG problem with and without applying various prefiltering methods, and evaluated our results using experimental data that came from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank. Epicardial electrograms were recorded during RV pacing using a 108-electrode array, simultaneously with ECGs from 128 electrodes embedded in the tank surface. Leave-one-beat-out protocol was used to obtain the prior probability density function (pdf) of electro-grams and noise statistics. Noise pdf was assumed to be zero mean-Gaussian, with covariance assumptions: a) independent and identically distributed (noi-iid), b) correlated (noi-corr). Reconstructed electrograms and activation times were compared to those directly recorded by the sock for 3 beats selected from the recording. Noi-corr is superior to noi-iid when the training set is a good match to data, but for applications requiring activation time derivation, careful selection of preprocessing methods, in particular to adequately remove high-frequency noise, and an appropriate noise model is needed.

Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography

The inverse problem of electrocardiography is ill-posed. Errors in the model such as signal noise can impact the accuracy of reconstructed cardiac electrical activity. It is currently not known how sensitive the inverse problem is to signal processing techniques. To evaluate this, experimental data from a Langendorff-perfused pig heart (n=1) suspended in a human-shaped torso-tank was used. Different signal processing methods were applied to torso potentials recorded from 128 electrodes embedded in the tank surface. Processing methods were divided into three categories i) high-frequency noise removal ii) baseline drift removal and iii) signal averaging, culminating in n=72 different signal sets. For each signal set, the inverse problem was solved and reconstructed signals were compared to those directly recorded by the sock around the heart. ECG signal processing methods had a dramatic effect on reconstruction accuracy. In particular, removal of baseline drift significantly impacts the magnitude of reconstructed electrograms, while the presence of high-frequency noise impacts the activation time derived from these signals (p<0.05).

Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications

Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.

The roles of mid-myocardial and epicardial cells in T-wave alternans development: a simulation study

Background

The occurrence of T-wave alternans in electrocardiographic signals was recently linked to susceptibility to ventricular arrhythmias and sudden cardiac death. Thus, by detecting and comprehending the origins of T-wave alternans, it might be possible to prevent such events.

Results

Here, we simulated T-wave alternans in a computer-generated human heart model by modulating the action potential duration and amplitude during the first part of the repolarization phase. We hypothesized that changes in the intracardiac alternans patterns of action potential properties would differentially influence T-wave alternans measurements at the body surface. Specifically, changes were simulated globally in the whole left and right ventricles to simulate concordant T-wave alternans, and locally in selected regions to simulate discordant and regional discordant, hereinafter referred to as “regional”, T-wave alternans. Body surface potential maps and 12-lead electrocardiographic signals were then computed. In depth discrimination, the influence of epicardial layers on T-wave alternans development was significantly higher than that of mid-myocardial cells. Meanwhile, spatial discrimination revealed that discordant and regional action potential property changes had a higher influence on T-wave alternans amplitude than concordant changes. Notably, varying T-wave alternans sources yielded distinct body surface potential map patterns for T-wave alternans amplitude, which can be used for location of regions within hearts exhibiting impaired repolarization. The highest ability for T-wave alternans detection was achieved in lead V1. Ultimately, we proposed new parameters Vector Magnitude Alternans and Vector Angle Alternans, with higher ability for T-wave alternans detection when using multi-lead electrocardiographic signals processing than for single leads. Finally, QT alternans was found to be associated with the process of T-wave alternans generation.

Conclusions

The distributions of the body surface T-wave alternans amplitude have been shown to have unique patterns depending on the type of alternans (concordant, discordant or regional) and the location of the disturbance in the heart. The influence of epicardial cells on T-wave alternans development is significantly higher than that of mid-myocardial cells, among which the sub-endocardial layer exerted the highest influence. QT interval alternans is identified as a phenomenon that correlate with T-wave alternans.

Geometrical constraint of sources in noninvasive localization of premature ventricular contractions

The inverse problem of electrocardiography for localization of a premature ventricular contraction (PVC) origin was solved and compared for three types of the equivalent cardiac electrical generator: transmembrane voltages, epicardial potentials, and dipoles. Instead of regularization methods usually used for the ill-posed inverse problems an assumption of a single point source representative of the heart generator was applied to the solution as a geometrical constraint. Body surface potential maps were simulated from eight modeled origins of the PVC in the heart model. Then the maps were corrupted by additional Gaussian noise with the signal-to-noise ratio (SNR) from 20 to 10dB and used as the input of the inverse solution. The inverse solution was computed from the first 30ms of the ventricular depolarization. It was assumed that during this period only a small part of the heart volume is activated thus it can be represented by a single point electrical source. Generally, the localization error was more dependent on the PVC origin position than on the type of the used heart generator. The most stable localization error between the inversely found results and the true PVC origin was not larger than 20mm for PVC origins located in the left ventricular wall and on the right ventricular anterior side. For such cases, the localization was robust to the noise up to SNR of 10dB for all studied types of the cardiac generator. For SNR 10dB the results became unstable mainly for the PVC origins in the septum and posterior right ventricle for the dipolar heart generator and for epicardial potentials defined on the pericardium when the range of the localization error increased up to 50mm. When the results for different electrical heart generators were considered altogether, the mean radius of the cloud of results did not exceed 20mm and the localization error of the cloud center was smaller than that obtained for a particular type of the cardiac generator. Combination of results from different models of a single point cardiac electrical generator can provide better information for the preliminary noninvasive localization of PVC than the use of one type of the generator.

Individualized model of torso surface for the inverse problem of electrocardiology

PURPOSE

We studied the implementation of a patient-specific torso model created without the use of magnetic resonance imaging in the inverse problem of electrocardiology.

METHOD

Three types of inhomogeneous numerical torso models were created, with different degrees of adjustment of the outer surface to patients, whereas the heart and lung models remained unchanged. The torso models were used in the inverse localization of small areas with repolarization changes from simulated difference integral QRST maps. The localization error (LE) was evaluated as the distance between the centers of the modeled and the inversely found area with repolarization changes.

RESULTS

The mean LE was 1.88 cm with the standard torso model. After adapting the torso shape, the mean LE was 1.83 cm, whereas after adapting both, the shape and electrode positions, the mean LE was 1.02 cm.

CONCLUSION

If torso imaging is not available, a torso model with adapted shape and electrode positions gives only slightly less accurate results.

Noninvasive detection of repolarization changes in the heart

OBJECTIVE

Previously reported inverse method based on dipolar representation of differences in QRST integral maps with and without manifestation of local repolarization changes has shown the ability to identify small areas in the myocardium responsible for these changes in a group of patients with coronary artery diseases underwent revascularization. The aim of this study was to verify this approach on a group of 4 healthy persons and a group of 7 patients suffering from effort angina pectoris.

METHODS

Changes in QRST integral maps after nitroglycerine sublingual application were examined and single dipole best representing the difference QRST integral map was inversely computed.

RESULTS

After attempted compensation of heart rate variations, changes in QRST integral maps greater than expected intra - individual variability (over 15%) were detected in 4 persons. Obtained difference integral maps could be sufficiently approximated by maps generated by single current dipole only in 2 persons with relative root mean square (rms) error less than 35%; in the rest of subjects relative rms error of the dipolar map approximation was greater than 50%.

CONCLUSION

Results suggest that small repolarization changes might be detectable after nitroglycerine test, however this test did not induce detectable changes in some patients with effort angina pectoris.