Body Surface Signals Research Articles

Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging

Objective. Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI. Approach. We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established ‘joint-inverse’ formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets. Main results. We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution. Significance. This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.

Robustness of electrocardiographic imaging in the presence of electrical noise

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Ministerio de Ciencia, Innovación y Universidades; Generalitat Valenciana Background Electrocardiographic Imaging (ECGI) allows evaluating the cardiac substrate non-invasively. However, the inverse solution is strongly dependent on the noise of the body surface signals. Zero-order Tikhonov regularization together with L-curve optimization, widely used in ECGI studies, allows minimizing the sources of noise to find an optimal solution. Objective Evaluate the effect of electrical and noise on the L-curve optimization in computer simulations of atrial activity and in the physiological interpretation of the inverse solution. Methods Five atrial simulations of regular rhythms (3 atrial flutter and 2 sinus rhythm) were used for computing the ECGI with five signal-to-noise ratios (SNR) (from 3dB to 40dB). ECGI was computed by zero-order Tikhonov regularization and L-curve optimization. The regularization parameter (λ) and the shape of the curve were analysed. Electrical noise was added to signals from an atrial flutter patient to study the effect on real ECGI signals. Local activation time (LAT) maps were obtained to evaluate the quality of the ECGI in front of noise. Correlation coefficient (CC) of LAT maps was computed between simulated electrograms and ECGIs. Results The level of noise was found to be correlated with the optimal regularization parameter (λ), (SNR 3dB, λ>10-6, SNR 40dB, λ<10-9). LAT maps for the optimal λ and higher were able to find relevant information about the simulation and patient independently on the SNR (CC3dB-EGM = 0.66±0.1, CC40dB-EGM = 0.76±0.1). Similar behaviour was observed in real atrial flutter signals (CC3dB-Ref = 0.83, CC10dB-Ref = 0.93). LAT maps from the atrial flutter were able to accurately locate the reentry circuit, as found in the electroanatomical map independently on the SNR. Conclusion LAT maps derived from ECGI are robust against the presence of noise. Although the reconstructed potentials are smoother with increasing levels of noise, LAT maps with and without noise show a good correlation thanks to an increased regularization. Figure 1. A. Local activation times (LAT) map of a reference electrogram (EGM) from a computer simulation of sinus rhythm in the atria. B. Electrocardiographic imaging maps (ECGI) obtained from the simulation A for different levels of electrical noise and regularization parameters. C. Example of LAT map obtained with ECGI and the endocardial map of a typical atrial flutter together with LAT maps with increased electrical noise but comparable results (CC > 0.83).

Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation.

Introduction: Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and recording ECG electrodes distribution to compute ECGI during atrial fibrillation (AF). Methods: Torso meshes from 100 to 2000 nodes heterogeneously and homogeneously distributed were compared. Signals from nine AF realistic mathematical simulations were used for computing the ECGI. Results for each torso mesh were compared with the ECGI computed with a 4,000 nodes reference torso. In addition, real AF recordings from 25 AF patients were used to compute ECGI in torso meshes from 100 to 1,000 nodes. Results were compared with a reference torso of 2000 nodes. Torsos were remeshed either by reducing the number of nodes while maximizing the overall shape preservation and then assigning the location of the electrodes as the closest node in the new mesh or by forcing the remesher to place a node at each electrode location. Correlation coefficients, relative difference measurements and relative difference of dominant frequencies were computed to evaluate the impact on signal morphology of each torso mesh. Results: For remeshed torsos where electrodes match with a geometrical node in the mesh, all mesh densities presented similar results. On the other hand, in torsos with electrodes assigned to closest nodes in remeshed geometries performance metrics were dependent on mesh densities, with correlation coefficients ranging from 0.53 ± 0.06 to 0.92 ± 0.04 in simulations or from 0.42 ± 0.38 to 0.89 ± 0.2 in patients. Dominant frequency relative errors showed the same trend with values from 1.14 ± 0.26 to 0.55 ± 0.21 Hz in simulations and from 0.91 ± 0.56 to 0.45 ± 0.41 Hz in patients. Conclusion: The effect of mesh density in ECGI is minimal when the location of the electrode is preserved as a node in the mesh. Torso meshes constructed without imposing electrodes to constitute nodes in the torso geometry should contain at least 400 nodes homogeneously distributed so that a distance between nodes is below 4 cm.

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.

Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes. Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits. One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve (AUROC) of 0.731, outperforming the existing APPLE scores (AUROC=0.644) and CHA2DS2-VASc scores (AUROC=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models. Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution.

Computational modeling of electrophysiological properties of the rabbit heart is a commonly used way to enhance and/or complement findings from classic lab work on single cell or tissue levels. Yet, thus far, there was no possibility to extend the scope to include the resulting body surface potentials as a way of validation or to investigate the effect of certain pathologies. Based on CT imaging, we developed the first openly available computational geometrical model not only of the whole heart but also the complete torso of the rabbit. Additionally, we fabricated a 32-lead ECG-vest to record body surface potential signals of the aforementioned rabbit. Based on the developed geometrical model and the measured signals, we then optimized the activation sequence of the ventricles, recreating the functionality of the Purkinje network, and we investigated different apico-basal and transmural gradients in action potential duration. Optimization of the activation sequence resulted in an average root mean square error between measured and simulated signal of 0.074 mV/ms for all leads. The best-fit T-Wave, compared to measured data (0.038 mV/ms), resulted from incorporating an action potential duration gradient from base to apex with a respective shortening of 20 ms and a transmural gradient with a shortening of 15 ms from endocardium to epicardium. By making our model and measured data openly available, we hope to give other researchers the opportunity to verify their research, as well as to create the possibility to investigate the impact of electrophysiological alterations on body surface signals for translational research.

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3–30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DFBSPM (HDFBSPM) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ≤1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). Main results: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. Significance: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.

Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from −0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to −4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

An optimized method for estimating the tidal volume from intracardiac or body surface electrocardiographic signals: implications for estimating minute ventilation.

The ability to accurately monitor tidal volume (TV) from electrocardiographic (ECG) signals holds significant promise for improving diagnosis treatment across a variety of clinical settings. The objective of this study was to develop a novel method for estimating the TV from ECG signals. In 10 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the coronary sinus (CS), left ventricle (LV), and right ventricle (RV), as well as body surface electrograms, while TV was varied between 0 and 750 ml at respiratory rates of 7-14 breaths/min. We devised an algorithm to determine the optimized respirophasic modulation of the amplitude of the ECG-derived respiratory signal. Instantaneous measurement of respiratory modulation showed an absolute error of 72.55, 147.46, 85.68, 116.62, and 50.89 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. Minute TV estimation demonstrated a more accurate estimation with an absolute error of 69.56, 153.39, 79.33, 122.16, and 48.41 ml for body surface, CS, LV, RV, and RV-CS leads, respectively. The RV-CS and body surface leads provided the most accurate estimations that were within 7 and 10% of the true TV, respectively. Finally, the absolute error of the bipolar RV-CS lead was significantly lower than any other lead configuration (P < 0.0001). In conclusion, we have demonstrated that ECG-derived respiratory modulation provides an accurate estimation of the TV using intracardiac or body surface signals, without the need for additional hardware.

An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation.

It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R(2) = 0.97), coronary sinus (R(2) = 0.96), left ventricular (R(2) = 0.96), and epicardial (R(2) = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R(2) = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R(2) = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

Cardiac electrical imaging from body surface potential measurements is increasingly being seen as a technology with the potential for use in the clinic, for example for pre-procedure planning or during-treatment guidance for ventricular arrhythmia ablation procedures. However several important impediments to widespread adoption of this technology remain to be effectively overcome. Here we address two of these impediments: the difficulty of reconstructing electric potentials on the inner (endocardial) as well as outer (epicardial) surfaces of the ventricles, and the need for full anatomical imaging of the subject's thorax to build an accurate subject-specific geometry. We introduce two new features in our reconstruction algorithm: a nonlinear low-order dynamic parameterization derived from the measured body surface signals, and a technique to jointly regularize both surfaces. With these methodological innovations in combination, it is possible to reconstruct endocardial activation from clinically acquired measurements with an imprecise thorax geometry. In particular we test the method using body surface potentials acquired from three subjects during clinical procedures where the subjects' hearts were paced on their endocardia using a catheter device. Our geometric models were constructed using a set of CT scans limited in axial extent to the immediate region near the heart. The catheter system provides a reference location to which we compare our results. We compare our estimates of pacing site localization, in terms of both accuracy and stability, to those reported in a recent clinical publication , where a full set of CT scans were available and only epicardial potentials were reconstructed.

Cardiac electrical activity-from heart to body surface and back again

We report here on our latest developments in the forward and inverse problems of electrocardiology. In the forward problem, a coupled cellular model of cardiac excitation-contraction is embedded within an anatomically realistic model of the cardiac ventricles, which is itself embedded within a torso model. This continuum modelling framework allows the effects of cellular-level activity on the surface electrocardiogram (ECG) to be carefully examined. Furthermore, the contributions of contraction and local ischemia on body surface recordings can also be elucidated. Such information can provide theoretical limits to the sensitivity and ultimately the detection capability of body surface ECG recordings. Despite being very useful, such detailed forward modelling is not directly applicable when seeking to use densely sampled ECG information to assess a patient in a clinical environment (the inverse problem). In such a situation patient specific models must be constructed and, due to the nature of the inverse problem, the level of detail that can be reliably reproduced is limited. Extensive simulation studies have shown that the accuracy with which the heart is localised within the torso is the primary limiting factor. To further identify the practical performance capabilities of the current inverse algorithms, high quality experimental data is urgently needed. We have been working towards such an objective with a number of groups, including our local hospital in Auckland. At that hospital, in patients undergoing catheter ablation surgery, up to 256 simultaneous body surface signals were recorded by using various catheter pacing protocols. The geometric information required to customize the heart and torso model was obtained using a combination of ultrasound and laser scanning technologies. Our initial results indicate that such geometric imaging modalities are sufficient to produce promising inversely-constructed activation profiles.

Analysis of PTCA-induced ischemia using an ECG inverse solution or the wavelet transform

In patients without significant collaterals, percutaneous transluminal coronary angioplasty (PTCA) produces acute transient ischemia that is detectable in both standard electrocardiograms (ECG) and body surface potential maps (BSPMs). Control recordings made before or between inflations provide personalized baselines, which isolate the effects of ischemia from interpatient differences, such as torso shape and electrode location. In this study, two methods of evaluating PTCA-induced ischemia from BSPM recordings are presented. In the first method, an ECG inverse solution that estimates epicardial potentials from body surface signals using a realistic model of torso geometry is applied. The strength of this method lies in its potential ability to localize areas of cardiac ischemia on the epicardial surface. In the second approach, wavelet transforms were used to perform a multiresolution decomposition of the BSPM data into different frequency bands. The basis functions of the wavelet transform are time-limited and narrow band and hence can be expected to be sensitive to features of the BSPM that originate in discrete electrophysiologic events, such as intrusion of the activation front onto regions of ischemia or arrhythmias due to local conduction abnormalities. The method also offers a means of temporal and frequency localization of cardiac events related to the initiation of injury currents and abnormal conduction due to PTCA-induced ischemia. The inverse solution and the wavelet transform each offer new views of the spatial and temporal courses of acute ischemia potentially leading to new diagnostic insights in ECG patient examination.

Beat to beat surface recording and averaging of his-purkinje activity in man

A non-invasive technique has been used to record the electrical activity of the His-Purkinje System (HPS) on the body surface involving shielding, high gain band pass filtering and averaging of bipolar chest ECGs (V6-V6R). The measurements were made in the Berlin Magnetically Shielded Room (BMSR) which reduces the background noise level (almost white) to less than 0.4 microVrms (measured for a bandwidth between 0.1 Hz and 300 Hz), so that individual HPS body surface signals were limited only by patient inherent noise. Signal amplitudes greater than 1 microV were detectable in single beats. In addition, for best resolution, signal averaging was accomplished with software triggers referred to either the QRS complex or the P wave. A P-wave trigger is of most interest in clinical use because averaging techniques, even in the case of A-V blocks of different degree, can be applied.