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

Modeling ventricular repolarization gradients in normal cases using the equivalent dipole layer

Background Electrical activity underlying the T-wave is less well understood than the QRS-complex. This study investigated the relationship between normal T-wave morphology and the underlying ventricular repolarization gradients using the equivalent dipole layer (EDL). Methods Body-surface-potential-maps (BSPM, 67‑leads) were obtained in nine normal cases. Subject specific MRI-based anatomical heart/torso-models with electrode positions were created. The boundary element method was used to account for the volume conductor effects. To simulate the measured T-waves, the EDL was used to apply different ventricular repolarization gradients: a) transmural, b) interventricular c) apico-basal and d) all three gradients (a-c) combined. The combined gradient (d) was optimized using an inverse procedure (Levenberg-Marquardt). Correspondence between simulated and measured T-waves was assessed using correlation coefficient (CC) and relative difference (RD). Results Realistic T-waves were simulated if repolarization times of: (a) the epicardium were smaller than the endocardium; (b) the left ventricle were smaller than the right ventricle and (c) the apex increased towards the base. The apico-basal gradient resulted in the highest correspondence between measured and simulated T-waves (CC = 0.84(0.81-0.91);RD = 0.68(0.60-0.71)) compared to a transmural gradient (CC = 0.77(0.71-0.80);RD = 1.46(0.82-1.75)) and an interventricular gradient (CC = 0.71(0.67-0.80);RD = 0.85(0.75-0.87)). All three gradients combined further improved the correspondence between measured and simulated T-waves (CC = 0.83(0.82-0.89);RD = 0.60(0.51-0.63)), especially after optimization (CC = 0.96(0.94-0.98);RD = 0.27(0.22-0.34)). Conclusion The application of all repolarization gradients combined resulted in the largest agreement between simulated and measured T-waves, followed by the apico-basal repolarization gradient. With these findings, we will optimize our EDL-based inverse procedure to assess repolarization abnormalities.

Basis and applicability of noninvasive inverse electrocardiography: a comparison between cardiac source models

The body surface electrocardiogram (ECG) is a direct result of electrical activity generated by the myocardium. Using the body surface ECGs to reconstruct cardiac electrical activity is called the inverse problem of electrocardiography. The method to solve the inverse problem depends on the chosen cardiac source model to describe cardiac electrical activity. In this paper, we describe the theoretical basis of two inverse methods based on the most commonly used cardiac source models: the epicardial potential model and the equivalent dipole layer model. We discuss similarities and differences in applicability, strengths and weaknesses and sketch a road towards improved inverse solutions by targeted use, sequential application or a combination of the two methods.

Localization of the ventricular pacing site from BSPM and standard 12-lead ECG: a comparison study

Inverse ECG imaging methods typically require 32-250 leads to create body surface potential maps (BSPM), limiting their routine clinical use. This study evaluated the accuracy of PaceView inverse ECG method to localize the left or right ventricular (LV and RV, respectively) pacing leads using either a 99-lead BSPM or the 12-lead ECG. A 99-lead BSPM was recorded in patients with cardiac resynchronization therapy (CRT) during sinus rhythm and sequential LV/RV pacing. The non-contrast CT was performed to localize precisely both ECG electrodes and CRT leads. From a BSPM, nine signals were selected to obtain the 12-lead ECG. Both BSPM and 12-lead ECG were used to localize the RV and LV lead, and the localization error was calculated. Consecutive patients with dilated cardiomyopathy, previously implanted with a CRT device, were enrolled (n = 19). The localization error for the RV/LV lead was 9.0 [IQR 4.8-13.6] / 7.7 [IQR 0.0-10.3] mm using the 12-lead ECG and 9.1 [IQR 5.4-15.7] / 9.8 [IQR 8.6-13.1] mm for the BSPM. Thus, the noninvasive lead localization using the 12-lead ECG was accurate enough and comparable to 99-lead BSPM, potentially increasing the capability of 12-lead ECG for the optimization of the LV/RV pacing sites during CRT implant or for the most favorable programming.

Assessment of electrical dyssynchrony in cardiac resynchronization therapy: 12-lead electrocardiogram vs. 96-lead body surface map

AIMS

The standard deviation of activation time (SDAT) derived from body surface maps (BSMs) has been proposed as an optimal measure of electrical dyssynchrony in patients with cardiac resynchronization therapy (CRT). The goal of this study was two-fold: (i) to compare the values of SDAT in individual CRT patients with reconstructed myocardial metrics of depolarization heterogeneity using an inverse solution algorithm and (ii) to compare SDAT calculated from 96-lead BSM with a clinically easily applicable 12-lead electrocardiogram (ECG).

METHODS AND RESULTS

Cardiac resynchronization therapy patients with sinus rhythm and left bundle branch block at baseline (n = 19, 58% males, age 60 ± 11 years, New York Heart Association Classes II and III, QRS 167 ± 16) were studied using a 96-lead BSM. The activation time (AT) was automatically detected for each ECG lead, and SDAT was calculated using either 96 leads or standard 12 leads. Standard deviation of activation time was assessed in sinus rhythm and during six different pacing modes, including atrial pacing, sequential left or right ventricular, and biventricular pacing. Changes in SDAT calculated both from BSM and from 12-lead ECG corresponded to changes in reconstructed myocardial ATs. A high degree of reliability was found between SDAT values obtained from 12-lead ECG and BSM for different pacing modes, and the intraclass correlation coefficient varied between 0.78 and 0.96 (P < 0.001).

CONCLUSION

Standard deviation of activation time measurement from BSM correlated with reconstructed myocardial ATs, supporting its utility in the assessment of electrical dyssynchrony in CRT. Importantly, 12-lead ECG provided similar information as BSM. Further prospective studies are necessary to verify the clinical utility of SDAT from 12-lead ECG in larger patient cohorts, including those with ischaemic cardiomyopathy.

The ΔWaveECG: The differences to the normal 12‑lead ECG amplitudes

BACKGROUND

The QRS, ST segment, and T-wave waveforms of electrocardiogram are difficult to interpret, especially for non-ECG experts readers, like general practitioners. As the ECG waveforms are influenced by many factors, like body build, age, sex, electrode placement, even for experience ECG readers the waveform is difficult to interpret. In this research we have created a novel method to distinguish normal from abnormal ECG waveforms for an individual ECG based on the ECG amplitude distribution derived from normal standard 12‑lead ECG recordings.

AIM

Creation of a normal ECG amplitude distribution to enable the distinction by non-ECG experts of normal from abnormal waveforms of the standard 12‑lead ECG.

METHODS

The ECGs of healthy normal controls in the PTB-XL database were used to construct a normal amplitude distribution of the 12 lead ECG for males and females. All ECGs were resampled to have the same number of samples to enable the classification of an individual ECG as either normal or abnormal, i.e. within the normal amplitude distribution or outside, the ΔWaveECG.

RESULTS

From the same PTB-XL database six ECG's were selected, normal, left and right bundle branch block, and three with a myocardial infarction. The normal ECG was obviously within the normal distribution, and all other five showed clear abnormal ECG amplitudes outside the normal distribution in any of the ECG segments (QRS, ST segment and remaining STT segment).

CONCLUSION

The ΔWaveECG can distinguish the abnormal from normal ECG waveform segments, making the ECG easier to classify as normal or abnormal. Conduction disorders and ST changes due to ischemia and abnormal T-waves are effortless to detect, also by non-ECG expert readers, thus improving the early detection of cardiac patients.

Non-invasive estimation of QLV from the standard 12-lead ECG in patients with left bundle branch block

Background: Cardiac resynchronization therapy (CRT) is a treatment for patients with heart failure and electrical dyssynchrony, i.e., left bundle branch block (LBBB) ECG pattern. CRT resynchronizes ventricular contraction with a right ventricle (RV) and a left ventricle (LV) pacemaker lead. Positioning the LV lead in the latest electrically activated region (measured from Q wave onset in the ECG to LV sensing by the left pacemaker electrode [QLV]) is associated with favorable outcome. However, optimal LV lead placement is limited by coronary venous anatomy and the inability to measure QLV non-invasively before implantation. We propose a novel non-invasive method for estimating QLV in sinus-rhythm from the standard 12-lead ECG.

Methods: We obtained 12-lead ECG, LV electrograms and LV lead position in a standard LV 17-segment model from procedural recordings from 135 standard CRT recipients. QLV duration was measured post-operatively. Using a generic heart geometry and corresponding forward model for ECG computation, the electrical activation pattern of the heart was fitted to best match the 12-lead ECG in an iterative optimization procedure. This procedure initialized six activation sites associated with the His-Purkinje system. The initial timing of each site was based on the directions of the vectorcardiogram (VCG). Timing and position of the sites were then changed iteratively to improve the match between simulated and measured ECG. Noninvasive estimation of QLV was done by calculating the time difference between Q-onset on the computed ECG and the activation time corresponding to centroidal epicardial activation time of the segment where the LV electrode is positioned. The estimated QLV was compared to the measured QLV. Further, the distance between the actual LV position and the estimated LV position was computed from the generic ventricular model.

Results: On average there was no difference between QLV measured from procedural recordings and non-invasive estimation of QLV (ΔQLV=−3.0±22.5 ms, p=0.12). Median distance between actual LV pacing site and the estimated pacing site was 18.6 mm (IQR 17.3 mm).

Conclusion: Using the standard 12-lead ECG and a generic heart model it is possible to accurately estimate QLV. This method may potentially be used to support patient selection, optimize implant procedures, and to simulate optimal stimulation parameters prior to pacemaker implantation.