Atrial fibrillation substrate mapping with decrement evoked potential mapping

Funding Acknowledgements

Type of funding sources: Foundation. Main funding source(s): Sociedad Española de Cardiologia.

OnBehalf

DEFINE-AF

Background

Identifying and targeting atrial substrate zones that are vulnerable to unidirectional block and slow conduction may be critical to improve the outcomes of atrial fibrillation (AF) ablation. Functional mapping of the atrial substrate with Decrement Evoked Potential (DeEP) and a single extrastimulus in this population could potentially lead to novel therapeutic strategies.

Aim

1) To systematically analyze whether the DEEP are present in the atrial tissue and their locations after pulmonary vein isolation. 2) To assess their relationship with the underlying voltage.  3) To assess the presence of DEEP as a function of the subtype of AF.

Methods

Consecutive patients with AF undergoing ablation were prospectively enrolled at 3 institutions. A biatrial voltage map was created and after pulmonary vein isolation (PVI). A drive train and an extrastimulus (atrial refractory period + 20ms) was delivered from an epicardial site (proximal CS) and an endocardial site (left atrial appendage (LAA). A multipolar mapping catheter was sequentially placed at 8 left atrial sites and 5 right atrial sites. Electrograms (EGMs) that showed a local delay of >10ms in activation with the extrastimulus were identified as DEEPs. Patients were followed for a mean of 11 ± 5 months

Results

74 patients, 63 pers AF (85%), mean age 62 ± 8, mean LA size 41 ±12 mm were enrolled. Of 19240 EGMs analyzed, 8.2% showed DEEPs (54.6% with CS pacing and 45.4% with LAA pacing, p = 0.0001). The mean local decrement seen was 39 ms. Most DEEPs (76.2%) were identified in sites with a normal EGM at baseline with preserved voltages. DEEPs were differentially distributed within the regions mapped, more frequently in LA than RA (9.2% vs 6.6%, p < 0.0001). Patients with persistent AF had a higher proportion of DEEPs than patients with paroxysmal AF (9.7% vs 5.1%, p < 0.001).

Conclusions

Atrial DEEPs are: 1) More often identified when pacing endocardially. 2) More common in patients with persistent AF. 3) More frequent in the LA than in the RA. 4) Mostly located in regions with normal voltages at baseline. All those findings suggest the importance of the functional substrate mapping in the atrium and could lead to novel therapeutic targets. Abstract Figure. Example of atrial DEEP

Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias

Ventricular arrhythmias (VA) are life-threatening pathophysiological conditions that seriously impact the normal functioning of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two well known types of VA. VF is the lethal of the VAs and could be characterized by its organizational progression over time. The success of cardiac resuscitation strongly depends on the type of VA, its evolution over time and response to therapy. Due to the time critical nature of VF, computationally efficient quantification of VAs and swift feedback are essential. This work attempted to arrive at computationally efficient and data-driven techniques based on Empirical Mode Decomposition for classifying and tracking VAs over time. The approaches are divided into two aims: (1) 'in-hospital' scenarios for characterizing the dynamics of VA episodes to assist clinicians in planning long-term therapy options, and (2) 'out-of-hospital' scenarios for providing near real-time feedback to detect/track the progression of VAs over time to assist medical personnel select/modify therapy options. Using an ECG database of 61 60-s VA segments obtained for classifying VT vs. VF and sub-classifying VF into organized VF (OVF) and disorganized VF (DVF), maximum classification accuracies of 96.7% (AUC = 0.993) and 87.2% (AUC = 0.968) were obtained for classifying VT vs. VF and OVF vs. DVF during 'in-hospital' analysis. Additionally, two near real-time approaches were presented for 'out-of-hospital' analysis where average accuracies of 71% and 73% were achieved for VT/VF and OVF/DVF classification, as well as demonstrating strong potential for monitoring VA progressions over time.

Modeling Current Density Maps Using Aliev-Panfilov Electrophysiological Heart Model

Most existing studies of cardiac arrhythmia rely on surface measurements through optical or electrical mapping techniques. Current density imaging (CDI) is a method which enables us to study current pathways inside the tissue. However, this method entails implementation complexities for beating ex vivo hearts. Hence, this work presents an approach to simulate and study the current distributions in different cardiac electrophysiological states. The results are corroborated by experimental data, and they indicate that different states were distinguishable. The CDI simulations can be used for studying cardiac arrhythmias under simulation conditions which are otherwise impossible or difficult to be implemented experimentally.

Fusion of structural and functional cardiac magnetic resonance imaging data for studying ventricular fibrillation

Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.

A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart

Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.

The effects of long chain polyunsaturated fatty acids on local activation properties in dogs vulnerable to atrial fibrillation

Marine derived long chain polyunsaturated fatty acids (PUFAs) were found to have benefits in reducing inducibility and maintenance of atrial fibrillation (AF) in a dog model. This study was conducted to evaluate the effect of PUFAs on local atrial electrical conduction properties acquired via a multi-electrode plaque sutured to the posterior wall of the left atrium of the heart in these dogs. Eleven dogs underwent simultaneous atrioventricular pacing (SAVP) for 2 weeks, and were organized into 2 groups: 5 dogs received no PUFAs (SAVP-PLACEBO), 6 dogs received Eicosapentaenoic or Docosahexaenoic acid derived from fish oils (SAVP-PUFA), where PUFAs were given for 21 days, starting 1 week prior to pacing and during the 2 week pacing period. Three features were extracted, which were the average conduction velocity, average intra atrial conduction time, and total activation time. The PUFA group had a faster average conduction velocity (0.82±0.19 m/s) than the PLACEBO group (0.47±0.21 m/s, P=0.02). Using the average conduction velocity feature, classification was performed with a linear classifier and leave-one-out method. In the SAVP-PLACEBO group, 60% of the dogs were correctly classified, and 66% of the dogs were correctly classified in SAVP-PUFA group, leading to an overall classification accuracy of 63.5%.

Blind source separation in characterizing ECG pre-shock waveforms during Ventricular Fibrillation

Ventricular Fibrillation (VF) is a cardiac arrhythmia for which the only available treatment option is defibrillation by electrical shock. Existing literature indicates that VF could be the manifestation of different sources controlling the heart with different degrees of organization. In this work we test the hypothesis that the pre-shock waveforms of successful and unsuccessful shock outcomes could be related to the number of independent sources present in these waveforms. The proposed method uses Blind Source Separation (BSS) to extract independent components in frequency direction from a pig database consisting of 20 pre-shock waveforms. The slope of the energy capture curve was used as an indicator to demonstrate the number of independent sources required to model the pre-shock waveforms. The results were also quantified by performing a linear discriminant analysis based classification achieving an overall classification accuracy of 75%. The results indicate that successful cases can be modeled with less number of independent sources compared to unsuccessful cases.

Analysis of reliability metrics and quality enhancement measures in current density imaging

Low frequency current density imaging (LFCDI) is a magnetic resonance imaging (MRI) technique which enables calculation of current pathways within the medium of study. The induced current produces a magnetic flux which presents itself in phase images obtained through MRI scanning. A class of LFCDI challenges arises from the subject rotation requirement, which calls for reliability analysis metrics and specific image registration techniques. In this study these challenges are formulated and in light of proposed discussions, the reliability analysis of calculation of current pathways in a designed phantom and a pig heart is presented. The current passed is measured with less than 5% error for phantom, using CDI method. It is shown that Gauss's law for magnetism can be treated as reliability metric in matching the images in two orientations. For the phantom and pig heart the usefulness of image registration for mitigation of rotation errors is demonstrated. The reliability metric provides a good representation of the degree of correspondence between images in two orientations for phantom and pig heart. In our CDI experiments this metric produced values of 95% and 26%, for phantom, and 88% and 75% for pig heart, for mismatch rotations of 0 and 20 degrees respectively.

Studying semblances of a true killer: experimental model of human ventricular fibrillation

It is unknown whether ventricular fibrillation (VF) studied in experimental models represents in vivo human VF. First, we examined closed chest in vivo VF induced at defibrillation threshold testing (DFT) in four patients with ischemic cardiomyopathy pretransplantation. We examined VF in these same four hearts in an ex vivo human Langendorff posttransplantation. VF from DFT was compared with VF from the electrodes from a similar region in the right ventricular endocardium in the Langendorff using two parameters: the scale distribution width (extracted from continuous wavelet transform) and VF mean cycle length (CL). In a second substudy group where multielectrode phase mapping could be performed, we examined early VF intraoperatively (in vivo open chest condition) in three patients with left ventricular cardiomyopathy. We investigated early VF in the hearts of three patients in an ex vivo Langendorff and compared findings with intraoperative VF using two metrics: dominant frequency (DF) assessed by the Welch periodogram and the number of phase singularities (lasting >480 ms). Wavelet analysis (P = 0.9) and VF CL were similar between the Langendorff and the DFT groups (225 ± 13, 218 ± 24 ms; P = 0.9), indicating that wave characteristics and activation rate of VF was comparable between the two models. Intraoperative DF was slower but comparable with the Langendorff DF over the endocardium (4.6 ± 0.1, 5.0 ± 0.4 Hz; P = 0.9) and the epicardium (4.5 ± 0.2, 5.2 ± 0.4 Hz; P = 0.9). Endocardial phase singularity number (9.6 ± 5, 12.1 ± 1; P = 0.6) was lesser in number but comparable between in vivo and ex vivo VF. VF dynamics in the limited experimental human studies approximates human in vivo VF.

Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia that if untreated within minutes of its occurrence will lead to sudden cardiac death. Defibrillation using electric shocks is the only choice of treatment to restore the heart to normal rhythm especially in out-of-the-hospital VF incidents. Refibrillation (i.e., recurrence of VF) is a common and significant problem in cardiac resuscitation as it negatively impacts the survival rates. In such refibrillation cases administration of anti-arrhythmic drugs could improve the shock outcomes or prevent refibrillation. In cases of prolonged VF, cardio pulmonary resuscitation (CPR) prior to the shocks have been shown to improve the survival rates. The proposed work using wavelet analysis of the pre-shock VF electrograms attempts to predict the shock outcomes as successful, refibrillation, and unsuccessful categories. This feedback in real-time would be of immense assistance to the Emergency Medical Services (EMS) personnel in choosing the right combination of therapies (i.e., shock, CPR, pharmacology interventions) in improving the shock outcomes. Using a real-word database of 34 pre-shock VF electrograms obtained from Toronto area EMS personnel, the proposed method achieved classification accuracies of 76.5% and 75% for a two level binary classification of the three groups.

Relating spatial heterogeneities to rotor formation in studying human ventricular fibrillation

Ventricular fibrillation (VF) occurs due to disorganized electrical activity in the ventricles. This leads to rapid uncoordinated contractions of the ventricles and sudden cardiac death if not treated within minutes of its occurrence. The mechanism of VF initiation and maintenance is still elusive, however the mother rotor and multiple wavelet theories attempt to explain the mechanism behind this lethal arrhythmia. In mother rotor theory, VF is believed to be maintained by high frequency periodic sources called rotors that could be tracked using the phase progression along and through the myocardium using spatio-temporal electrical mapping of the heart. There are exiting works including our previous works that have related the formation of these rotors to anatomical and physiological heterogeneities observed in the myocardium. In this study we performed an correlation exercise of the locations of rotors with scar boundary maps and dominant frequency maps and elucidated this relation using human VF data acquired from isolated human hearts. The results suggest that in 14 rotors over 6 human hearts that we studied, all rotors co-localized to boundary zones of scar and low-high dominant frequency locations. The mean variance of the dominant frequency over the spatial location of the rotor was found to be 0.55 with average minimum of 4.15 Hz to a maximum of 5.71 Hz. This results in human VF data strongly suggest that boundary zones of healthy-non-healthy tissues and low-high frequency boundaries form a favorite substrate for rotor formation.