Tolkacheva, Elena G.

BACKGROUND: Ventricular fibrillation (VF) remains a leading cause of sudden cardiac death with an incompletely characterized electrophysiological substrate. Traditional large-animal VF models are limited by hemodynamic collapse and myocardial ischemia. This study establishes a reproducible swine model using dual mechanical circulatory devices to enable high-density electroanatomic mapping under nonischemic conditions. METHODS: Five Yorkshire swine (55–65 kg) underwent sustained VF supported by venoarterial extracorporeal membrane oxygenation (2.5–3.0 L/min) and percutaneous left ventricular unloading (Impella CP, P-2–P-4). High-density electroanatomic mapping using CARTO 7 was performed at baseline and after defibrillation. Bipolar voltage was acquired from 7 predefined ventricular regions (21 measurements per animal per time point; n=210 paired observations). Unipolar voltage mapping and continuous surface ECG monitoring were used to assess acute ischemic injury. Systemic perfusion was maintained at systolic arterial pressure ≥80 mm Hg. Linear mixed-effects modeling was used to analyze voltage changes from pre-VF to post-VF. RESULTS: Sustained VF (10±1 minutes) was reproducibly induced and maintained with consistent hemodynamics (systolic arterial pressure, 90±5 mm Hg; extracorporeal membrane oxygenation flow, 2.5±0.5 L/min). All animals were successfully defibrillated. High-density mapping was feasible in all experiments (1000±300 points per chamber). Bipolar voltage showed no change from pre-VF (5.86±2.56 mV) to post-VF (6.53±2.22 mV; mean difference, 0.67 mV [95% CI, –0.31 to 1.65]; P =0.15). Both left ( P =0.17) and right ventricles ( P =0.08) demonstrated voltage preservation. Regional analysis showed no significant changes (all P >0.05); post-VF voltages remained above viability thresholds (>1.5 mV). Unipolar voltage maps recovered toward baseline, with no ECG injury pattern identified after defibrillation. CONCLUSIONS: Dual mechanical circulatory support with venoarterial extracorporeal membrane oxygenation and Impella CP enables reproducible, high-fidelity electroanatomic mapping and preserves myocardial substrate after sustained VF in a large-animal model. This platform overcomes key technical barriers in VF research, providing a nonischemic foundation for future mechanistic and translational studies.

Abstract Medical devices that use electromagnetic fields for diagnostic and therapeutic purposes are rapidly evolving. Understanding the interactions between electromagnetic signals and biological tissue is fundamental to developing safe, novel diagnostic and therapy treatments. This study characterizes the dielectric properties of porcine hearts from 10 male and 10 female animals. Dielectric properties are measured using an open-ended coaxial probe dielectric spectroscopy method over a frequency range of 0.5 to 20 GHz. Scatter parameters (S11) collected from various anatomical cardiac locations (LA, RA, LV, RV, SA, IAS, IVS) are converted into frequency-dependent complex permittivity. The calculated dielectric properties vary regionally within the heart, indicating heterogeneity in tissue properties observed at microwave frequencies.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

Background: Rigorous study of ventricular fibrillation (VF) is not feasible in humans. The spatiotemporal characteristics of prolonged VF remain undefined, limiting our understanding of this lethal rhythm. Methods: VF was mapped in 4 canines. The endocardial and epicardial left ventricle (LV) and right ventricle (RV) were sequentially mapped at 0 to 15, 15 to 30, 30 to 45, and 45 to 60 minutes post-induction. Ten consecutive beats were used to determine the average cycle length and regularity index of ventricular and His-Purkinje system signals in discrete regions during each time interval. Results: Average VF time was 58±12 minutes. The shortest ventricular cycle length was present in the RV apical region (70±10 msec) at 0 to 15 minutes and at 15 to 30 minutes (89±31 msec) and LV apical region at 45 to 60 minutes (242±163 msec). The His-Purkinje system cycle length was the shortest at the RV outflow tract (75±3 msec) at 0 to 15 minutes, RV inflow and free wall (89±12 msec) at 15 to 30 minutes, LV apical region (83±14 msec) at 30 to 45 minutes, and inferior and inferolateral LV (145±23 msec) at 45 to 60 minutes. Regularity index was the highest in the RV inflow and free wall (78%) at 0 to 15 minutes, RV apical region (86%) at 15 to 30 minutes, LV septum and epicardial anterior RV (80%) at 30 to 45 minutes, and anterior and anterolateral LV (75%) at 45 to 60 minutes. Conclusions: These data suggest significant regional changes in electrical activity throughout VF in canines. A transition of fastest electrical activity from RV to LV apical regions across VF was observed. These novel insights of the temporal and spatial changes of VF merit further evaluation in diseased hearts to assess for reproducibility of findings.

AbstractBackgroundCatheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF). Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several techniques (multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE]) to identify pivot point of rotors using ex‐vivo optical mapping animal experiments. However, the performance of these techniques is unclear for the clinically recorded intracardiac electrograms (EGMs), due to the different nature of the signals.ObjectiveThis study aims to evaluate the performance of MSF, MSE, SE, and Kt techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations.MethodsStationary and meandering rotors were simulated in a 2D human atria. The performances of new approaches were quantified by comparing the “true” core of the rotor with the core identified by the techniques. Also, the performances of all techniques were evaluated in the presence of noise, scar, and for the case of the multielectrode multispline and grid catheters.ResultsOur results demonstrate that all the approaches are able to accurately identify the pivot point of both stationary and meandering rotors from both unipolar and bipolar EGMs. The presence of noise and scar tissue did not significantly affect the performance of the techniques. Finally, the core of the rotors was correctly identified for the case of multielectrode multispline and grid catheter simulations.ConclusionThe core of rotors can be successfully identified from EGMs using novel techniques; thus, providing motivation for future clinical implementations.

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death [1]. 12-lead electrocardiogram (ECG) is used to monitor normal sinus rhythm (NSR) and also detect AF. Although the persistent form of AF can be detected relatively easy, detecting paroxysmal AF is often a challenge since requiring continuous monitoring, which becomes expensive and cumbersome to collect lot of ECG data [1]. Several researchers have attempted to develop new methods to discriminate NSR and AF which are based on R-R interval analysis, linear methods, filtering, spectral analysis, statistical approaches such as entropy etc. which faces limitation of successfully detecting AF of all types with high sensitivity and specificity using short time ECG data [1–3]. The major issues with these approaches is that they often distort the ECG by several pre-processing steps with filters, do not provide reliable discrimination using short ECG time series data and many of them lack real-time capability that makes it difficult to trust the data for diagnosis and treatment. Both clinical and scientific communities recognize these difficulties and the necessity to develop novel methods that can enable accurate monitoring and detection of AF [2]. In addition, robust detection and classification algorithms are essential for delivering appropriate therapy for implantable cardioverter defibrillators (ICD) to provide lifesaving timely action. In this work, the authors propose and demonstrate the application of a multiscale frequency (MSF) approach [4] for accurate detection and discrimination between AF and NSR ECG traces taken from publically available Physionet database. The MSF approach takes into account the contribution from various frequencies in ECG and thus yield valuable information regarding the chaotic nature of AF. Therefore, we demonstrate that MSF can capture the complexity of AF which is associated with higher MSF value compared with NSR thus enabling robust discrimination e AF manifests itself with numerous chaotic frequencies within the body surface ECG,. We validate the feasibility of this technique to discriminate NSR from AF.