Ventricular substrate identification using close-coupled paced electrogram feature analysis.

AIMS: Substrate based catheter ablation strategies are widely employed for treatment of scar-related ventricular tachycardia (VT). We analysed intracardiac electrograms (EGMs) from close-coupled paced extrastimuli extracted from the EnSite Precision mapping system. We sought to characterize EGM responses of ventricular myocardium to varying coupling intervals from the right ventricular apex (RVA) in both healthy individuals and patients presenting with VT for catheter ablation. METHODS AND RESULTS: Extrastimuli were delivered from the RVA after estimation of the ventricular effective refractory period. Electrograms were recorded from high-density mapping catheters in the left ventricle and exported for analysis to MATLAB. Observational data were collected from 14 patients with ischaemic VT (mean age 72.4 ± 6.3 years, one female) and five controls (mean age 59.4 ± 7.4 years, one female). These derived data were used to inform an interventional strategy on a further 10 patients (mean age 64.7 ± 10.0 years; two female). Significant differences were observed in EGM duration (ED) and latency (LT) at all coupling intervals between VT patients and controls. Significant increases in ED and LT with decreased RVA coupling interval were observed at VT isthmuses. Abnormal responses derived from control subject data were used to classify four types of ventricular EGM response. Targeting sites with abnormal LT and ED significantly reduced VT inducibility (5/14 derivation patients to 0/10 intervention patients; P = 0.03). CONCLUSION: Paced electrogram feature analysis is a novel tool to characterize the ischaemic substrate. Association with VT isthmuses and early ablation results suggest a possible role in substrate ablation for ischaemic VT.

Cardiac Conduction Velocity Estimation During Wavefront Collision.

Catheter ablation therapy is an effective approach to treat different arrhythmias. Cardiac conduction velocity (Cv), extracted from intracardiac electrograms, shows the speed and direction of the wavefront propagation at different sites and is an insightful feature to guide ablation therapy. To create a propagation map, a small mapping catheter with a high density of electrodes is usually used to sequentially collect electrograms from different sites in a desired chamber of the heart. The CV and isochrone surface estimations are very challenging during complex arrhythmias such as atrial fibrillation, where multiple wavefronts simultaneously excite different cardiac sites. Specifically, the performances of CV estimators significantly degrade at catheter sites where wave- fronts collide. This is mainly because during collision, different wavefronts pass the areas under different electrodes of the catheter. Consequently, the activation times of the electrodes are the results of different wavefronts, and there are sharp changes in isochrone line patterns in the vicinity of the collision's border. In this paper, we propose a method that is able to identify the collision sites and improve the estimation of CV and isochrone maps. The proposed method finds the electrodes of the catheter that are excited by a similar wavefront and then estimates the corresponding isochrone lines for that wavefront. Our simulation results confirmed the efficiency of the proposed method during collision.

Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial Fibrillation.

OBJECTIVE: We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). METHODS: We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 ± 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). RESULTS: Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 ± 10.5, 13 ± 15.5, and 4.2 ± 9.4 ms, respectively. CONCLUSION: The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. SIGNIFICANCE: The proposed methods can be used for real-time automated analysis of AF, the most challenging complex arrhythmia.

Cardiac conduction velocity estimation from sequential mapping assuming known Gaussian distribution for activation time estimation error.

In this paper, we study the problem of the cardiac conduction velocity (CCV) estimation for the sequential intracardiac mapping. We assume that the intracardiac electrograms of several cardiac sites are sequentially recorded, their activation times (ATs) are extracted, and the corresponding wavefronts are specified. The locations of the mapping catheter's electrodes and the ATs of the wavefronts are used here for the CCV estimation. We assume that the extracted ATs include some estimation errors, which we model with zero-mean white Gaussian noise values with known variances. Assuming stable planar wavefront propagation, we derive the maximum likelihood CCV estimator, when the synchronization times between various recording sites are unknown. We analytically evaluate the performance of the CCV estimator and provide its mean square estimation error. Our simulation results confirm the accuracy of the proposed method and the error analysis of the proposed CCV estimator.

Maximum likelihood cardiac conduction velocity estimation from sequential mapping in the presence of activation time noise with unknown variances.

The cardiac conduction velocity (CV) can be estimated by analysing the activation times (ATs) and the locations of the electrodes that are used for the intracardiac electrogram (IEGM) recording. Here, we study the problem of the CV estimation in sequential mapping without using any independent electrogram as a time alignment reference. We assume that the IEGMs are sequentially recorded from several sites, where at each site, at least two of the catheter's electrodes are in contact with the cardiac tissue. We consider the planar wavefront with stable CV that propagates within the recording sites throughout our data collection period. Assuming the zero-mean Gaussian AT estimation error, we derive the maximum likelihood estimations of the CV and AT at a desired location on the cardiac shell. The CV is estimated when the variance of the AT estimation error and the time delay between the sequential recordings are unknown variables. Our simulation results show that the proposed method can precisely estimate the CV of the planar wavefront.