Origin Of Premature Ventricular Contractions Research Articles

Source Localization and Classification of Pulmonary Valve-Originated Electrocardiograms Using Volume Conductor Modeling with Anatomical Models.

Premature ventricular contractions (PVCs) are a common arrhythmia characterized by ectopic excitations within the ventricles. Accurately estimating the ablation site using an electrocardiogram (ECG) is crucial for the initial classification of PVC origins, typically focusing on the right and left ventricular outflow tracts. However, finer classification, specifically identifying the left cusp (LC), anterior cusp (AC), and right cusp (RC), is essential for detailed preoperative planning. This study aims to improve the accuracy of cardiac waveform source estimation and classification in 27 patients with PVCs originating from the pulmonary valve. We utilized an anatomical human model and electromagnetic simulations to estimate wave source positions from 12-lead ECG data. Time-series source points were identified for each measured ECG waveform, focusing on the moment when the distance between the estimated wave source and the pulmonary valve was minimal. Computational analysis revealed that the distance between the estimated wave source and the pulmonary valve was reduced to less than 1 cm, with LC localization achieving errors under 5 mm. Additionally, 74.1% of the subjects were accurately classified into the correct origin (LC, AC, or RC), with each origin demonstrating the highest percentage of subjects corresponding to the targeted excitation origin. Our findings underscore the novel potential of this source localization method as a valuable complement to traditional waveform classification, offering enhanced diagnostic precision and improved preoperative planning for PVC ablation procedures.

Machine Learning for Localization of Premature Ventricular Contraction Origins: A Review.

Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ectopic beats in the ventricles. Precision in localizing the origin of PVCs has long been a focal point in electrophysiology research. Machine learning (ML) has developed rapidly in the past two decades with increasingly widespread applications. With the increase of clinical data such as electrocardiograms (ECGs), computed tomography (CT), and magnetic resonance imaging (MRI), ML and its subfields, deep learning (DL), have become powerful analytical tools, playing an increasingly important role in electrophysiological research. In this review, we mainly provide an overview of the development of ML in the localization of PVC origins, including its applications, advantages, disadvantages, and future research directions. This information is intended to serve as a reference for clinicians and researchers, aiding them in better-utilizing ML techniques for the diagnosis and study of PVC origins.

Optimal electrode placements for localizing premature ventricular contractions using a single dipole cardiac source model

IntroductionThe inverse problem of electrocardiography describes non-invasively the electrical activity of the heart using potential recordings from tens to hundreds of torso electrodes. Regrettably, the use of numerous electrodes poses a challenge to its integration into routine clinical practice. MethodsOptimal electrode placements, ranging from 8 to 112 electrodes, were derived from the singular values of the transfer matrices computed for all feasible positions of a single dipole cardiac source across 12 patients with unique geometrical characteristics from the Bratislava dataset. The transfer matrices were computed using the boundary element method. Subsequently, these optimal electrode placements were used to compute the inverse solution for localizing the origin of premature ventricular contraction (PVC) with a single dipole cardiac source. The localization error (LE) was computed as the Euclidean distance between the true PVC origin, obtained through an invasive radiofrequency ablation, and the inverse solution. This enabled a direct comparison of LE computed for each optimal electrode placement with that from the full 128-electrode set. ResultsResults showed that subsets of electrodes, particularly 32 to 112, provided comparable localization accuracy (LE of 30.5 ± 15.0 mm and 26.8 ± 12.6 mm) to the full 128-electrode set (LE of 27.2 ± 11.5 mm). High errors were observed with 8 and 16-electrode placements (LE of 48.6 ± 21.3 mm and 41.0 ± 18.3 mm). ConclusionPrecise PVC localization can be achieved using strategically positioned subsets of electrodes, offering advantages in reduced preparation time, enhanced patient comfort, and improved cost-effectiveness of body surface potential mapping.

Enhancing origin prediction: deep learning model for diagnosing premature ventricular contractions with dual-rhythm analysis focused on cardiac rotation.

Several algorithms can differentiate inferior axis premature ventricular contractions (PVCs) originating from the right side and left side on 12-lead electrocardiograms (ECGs). However, it is unclear whether distinguishing the origin should rely solely on PVC or incorporate sinus rhythm (SR). We compared the dual-rhythm model (incorporating both SR and PVC) to the PVC model (using PVC alone) and quantified the contribution of each ECG lead in predicting the PVC origin for each cardiac rotation. This multicentre study enrolled 593 patients from 11 centres-493 from Japan and Germany, and 100 from Belgium, which were used as the external validation data set. Using a hybrid approach combining a Resnet50-based convolutional neural network and a transformer model, we developed two variants-the PVC and dual-rhythm models-to predict PVC origin. In the external validation data set, the dual-rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; P < 0.01), precision (0.73 vs. 0.55, respectively; P < 0.01), specificity (0.87 vs. 0.68, respectively; P < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; P = 0.03), and F1-score (0.77 vs. 0.68, respectively; P = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC. Our deep learning-based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin, considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation.

Explainable localization of premature ventricular contraction using deep learning-based semantic segmentation of 12-lead electrocardiogram.

Predicting the origin of premature ventricular contraction (PVC) from the preoperative electrocardiogram (ECG) is important for catheter ablation therapies. We propose an explainable method that localizes PVC origin based on the semantic segmentation result of a 12-lead ECG using a deep neural network, considering suitable diagnosis support for clinical application. The deep learning-based semantic segmentation model was trained using 265 12-lead ECG recordings from 84 patients with frequent PVCs. The model classified each ECG sampling time into four categories: background (BG), sinus rhythm (SR), PVC originating from the left ventricular outflow tract (PVC-L), and PVC originating from the right ventricular outflow tract (PVC-R). Based on the ECG segmentation results, a rule-based algorithm classified ECG recordings into three categories: PVC-L, PVC-R, as well as Neutral, which is a group for the recordings requiring the physician's careful assessment before separating them into PVC-L and PVC-R. The proposed method was evaluated with a public dataset which was used in previous research. The evaluation of the proposed method achieved neutral rate, accuracy, sensitivity, specificity, F1-score, and area under the curve of 0.098, 0.932, 0.963, 0.882, 0.945, and 0.852 on a private dataset, and 0.284, 0.916, 0.912, 0.930, 0.943, and 0.848 on a public dataset, respectively. These quantitative results indicated that the proposed method outperformed almost all previous studies, although a significant number of recordings resulted in requiring the physician's assessment. The feasibility of explainable localization of premature ventricular contraction was demonstrated using deep learning-based semantic segmentation of 12-lead ECG.Clinical trial registration: M26-148-8.

Imaging-derived tissue visualization guides catheter ablation of premature ventricular contractions

Abstract Background The presence of myocardial scarring is associated with worse outcomes after catheter ablation of premature ventricular contractions (PVCs). Three-dimensional substrate characterization is emerging for peri-procedural ablation guidance, but its value for tissue visualization during PVC ablation is unknown. Purpose The aim of the present study was to assess the impact of imaging-derived tissue visualization on lesion planning, procedural and clinical outcomes of PVC ablation. Methods Thirteen consecutive patients (92.3% male, 62.2±12.4 years) undergoing unipolar (n=8) or bipolar (n=5) PVC catheter ablation with integrated three-dimensional imaging from multidetector computed tomography or cardiac magnetic resonance imaging were included. All patients presented with assumed myocardial scarring due to an underlying cardiomyopathy and/or after previous ablation. Results Among all patients (30.8% (peri-)myocarditis, 20.0% ischemic, 15.4% dilated cardiomyopathy, 15.4% idiopathic, 7.7% nonischemic, 7.7% congenital heart disease) undergoing ablation of 20 PVC morphologies (median 1 (IQR 1-2) per patient; 75.0% non-outflow tract; 70.0% left-sided), myocardial scarring was present in 11/13 (84.6%). Wall thinning &amp;lt;5 mm covering an area of 36.1±17.8 cm² contained the PVC origin in 4/13 (30.8%) patients and was safely targeted with radiofrequency lesions with up to 45 W (maximum power: median 32 W (IQR 30-35)) (Figure 1). Imaging-guided bipolar ablation was performed in 5/8 (62.5%) patients with previously therapy-refractory PVCs including foci at the left ventricular summit and the right ventricular outflow tract each in 2 and the left coronary cusp in one patient (Figure 2). PVCs were efficiently suppressed with less radiofrequency lesions (5.4±3.8 vs. 55.8±36.9; p=0.0160) with shorter duration (4.1±3.2 min vs. 23.3±13.5 min; p=0.0152) compared to prior unipolar ablation. Bipolar lesions resulted in a higher impedance (158 Ω (IQR 101-174) vs. 106 Ω (IQR 99-124); p=0.0025), while requiring a lower temperature (36 °C (IQR 35-38) vs. 39 °C (IQR 36-43); p=0.0001) and less power (26 W (IQR 20-30) vs. 30 W (IQR 25-31); p=0.0376). Complete suppression of the clinical PVC was reached in all 13 patients. During a follow-up of 396.8±299.7 days, PVC burden decreased from 20.1±14.3% to 1.7±1.6% (p=0.0172). Conclusions Imaging-derived tissue visualization enables determination of myocardial wall thinning &amp;lt;5 mm as a substrate for recurrent PVCs, which can be safely targeted. Three-dimensional imaging efficiently guides high power lesion application and bipolar ablation of PVCs at anatomically challenging foci.Anatomically challenging fociImaging-guided bipolar ablation

Clinical accuracy of a 3-dimensional multielectrode non-invasive ECG mapping vest in premature ventricular contraction ablation procedures

Abstract Background Invasive 3D mapping is the current gold standard in determining premature ventricular contraction (PVC) localization. However, given its invasive nature, unawareness of PVC origin beforehand may lead to prolonged procedure times and increased complications risk. Purpose In this study, we compared the diagnostic accuracy of a non-invasive 3-dimensional mapping ECG vest (MECGV) with invasive 3D mapping and ablation successful points as a reference. We also evaluated 12-lead ECG based pre-procedural PVC localization accuracy of 2 expert operators blinded to clinical findings. Methods In 15 consecutive adult patients between the ages of 35 to 69 (male n: 9, median age: 48), MECGV and subsequently a 3D mapping with uneventful successful PVC RF catheter ablation were performed and included in the study. PVC localization estimations of MECGV method and the EP experts' locatization predictions were compared separately with invasive 3D ablation results using the Cohen's kappa method and with the help of a 16-segmentation model we created for the ease of the analyses . Results Results were presented on Table-1. MECGV was found to be highly compatible (Cohen's kappa 0.968, p&amp;lt;0.001) with the RF ablation results. However operators assessments were found to be less coherent with invasive 3D mapping (Operator 1 Cohen's kappa 0.571, p 0.033, Operator 2 Cohen's kappa 0.618, p: 0.019, respectively). MECGV accuracy was %87 in pinpointing the PVC focus. The only 2 localizations that MECGV failed were on the septal aspects of the heart (Picture-2 Failure of defining the focus on LVOT septal instead of RVOT septal) Conclusion MECGV method seems to be highly compatible with invasive 3D mapping techniques in detection of PVC localization and bests EP expert estimates in this regard. However, it also seem to have some pitfalls especially on septal aspects of the heart.Table-1 and segmentation schemePicture-1

SIMULATION OF PREMATURE VENTRICULAR CONTRACTIONS IN PATIENT SPECIFIC BIDOMAIN VENTRICULAR MODEL

The goal of the study was to simulate electrical activation of the heart ventricles and corresponding body surface potentials (BSPs) during premature ventricular contractions (PVC) using the patient specific realistic homogeneous model of cardiac ventricles and the torso. Real position of the initial ectopic activation during PVC was determined by intracardial measurement in the upper part of the right ventricle near the His bundle and confirmed by successful catheter ablation of the PVC origin. Simulated electrical activation in the ventricular model was started at the position of the initial ectopic activation as well as at several other sites at various distances from this position. The propagation of electrical activation in the ventricular model was modeled using bidomain reaction-diffusion (RD) equations with the ionic transmembrane current density defined by the modified FitzHugh-Nagumo (FHN) equations. The torso was modeled as a homogeneous passive volume conductor. The RD equations were numerically solved in the Comsol Multiphysics environment. Simulated ECG signals and BSPs were compared with those measured during PVC in a real patient. The polarity and shape of simulated and measured ECG leads as well as the BSP distribution during the PVC were in best agreement when the stimulated region was less than 10 mm from the position of the initial ectopic activation.

Abstract 15352: Deep Learning Model-Enabled Electrocardiogram to Localize Premature Ventricular Contractions in Patients Referred for Catheter Ablation

Introduction: Accurate pre-procedure localization of the premature ventricular contractions (PVCs) is critical in preparing for the ablation. So far, no tools exist for artificial intelligence guided PVC-localization. Hypothesis: Trained deep learning model will be able to predict the site of origin (SOO) of PVCs using 12 lead electrocardiogram (ECG). Methods: We analyzed 707 ECG-ablation pairs from 2403 patients who had at least one ECG flagged for PVCs in the 6 months prior to their procedure. First, we used a cohort of 100,000 ECGs without PVCs and 10,000 ECGs with PVCs to train a convolutional neural network (CNN) to isolate the ECG segments with PVCs in them from a 12-lead ECG (Figure 1). We then trained two models, one utilizing a 10-second 12-lead ECG, and the other with just the isolated PVC beats as input. The models were trained using the Keras framework with TensorFlow (Google). Results: The mean age of the patients in the cohort was 61.2 ± 15.0 years. 66.3 percent of patients underwent left ventricle ablation, 54.6 percent had outflow ablation, and 55.3 percent had ablation of sites with leftward axis (septal location for midline and left ventricle PVCs). The model trained on 10-second ECGs yielded validation and test AUCs of 0.941 and 0.931 for outflow, 0.898 and 0.891 for ventricle, and 0.863 and 0.846 for leftward axis (Figure 1A). The model trained on isolated segments containing PVC beats yielded validation and test AUCs of 0.966 and 0.957 for outflow, 0.879 and 0.907 for ventricle, and 0.846 and 0.829 for SOO for leftward axis (Figure 1B). Conclusions: We have trained an accurate convolutional neural network that can predict SOO using the surface ECG. Further fine-tuning would be needed to refine the model and develop prediction of specific sites.

Lead I R-wave indexes: A novel electrocardiographic criterion for distinguishing the origin of idiopathic premature ventricular contractions from the three subregions of the aortic sinus cusps

PurposeThe current study was conducted to investigate the electrocardiographic (ECG) characteristics of idiopathic premature ventricular contractions (PVCs) originating from the aortic sinus cusp (ASC) and establish a novel ECG criterion to discriminate PVCs originating from the right coronary cusp (RCC), left coronary cusp (LCC), and the left and right coronary cusp junction (LRJ). MethodsA retrospective analysis was performed on a total of 133 patients with idiopathic PVCs who underwent successful mapping and ablation. The sites of origin (SOO) were confirmed using fluoroscopy and a three-dimensional mapping system during radiofrequency catheter ablation (RFCA). Among the patients, 69 had PVCs originating from the LCC, 39 from the RCC, and 25 from the LRJ. Characteristics of surface 12‑lead electrocardiograms (ECGs) recorded during PVCs were analyzed. Q-, R-, S, and R'-wave amplitudes were measured in lead I, and the lead I R-wave indexes (IRa, IRb, IRc, IRd, and IRe) were derived by employing multiplication, subtraction, sum, and division operations on these ECG measurements. Notably, IRb and IRe demonstrated usefulness as ECG indexes for discriminating PVCs originating from RCC, LCC, and LRJ in the ASC. ResultsThe R- and S-wave amplitudes in lead I exhibited statistically significant differences among the three groups (P < 0.001 and P < 0.001, respectively). In discriminating PVCs originating from the RCC from the other two groups, IRb showed the largest area under the curve (AUC) of 0.813, as assessed by receiver operating characteristic (ROC) analysis, with a cutoff value of ≤0.5 indicating PVCs of RCC origin. The sensitivity and specificity were 80.3% and 78.7%, respectively. For discriminating PVCs arising from the LCC from those in the LRJ group, IRe exhibited the largest AUC of 0.801, with an optimal cutoff value of 0. An IRe value >0 indicated PVCs originating from the LRJ, while an IRe value ≤0 indicated PVCs originating from the LCC. The sensitivity and specificity of the IRe index were 84.0% and 70.7%, respectively. ConclusionLead I R-wave indexes provided simple and useful ECG criteria for discriminating PVCs originating from the LCC, RCC, and LRJ in the left ventricular outflow tract (LVOT).

Electrocardiogram and cardiac electrophysiology characteristics of the premature ventricular contractions originating from right ventricular outflow tract

Premature ventricular contractions (PVCs) are a common type of irregular heartbeat (arrhythmia). PVCs often cause of symptoms that reduced quality of life such as: palpitation, pounding or jumping, chest pain, fatigue, blank beats or missed beats… Besides that, PVCs can produce ventricular tachycardia, which can lead to life-threatening hemodynamic instability.[1]&#x0D; Research objectives: Evalute the electrocardiogram and electrophysiology charaterics of the PVCs from right ventricular outflow tract. &#x0D; Objects and research methods: Cross-sectional descriptive study of 145 patients.&#x0D; Research results: all patients had R waveform in: D2, D3, aVF and S wave dominant in: aVR, aVL; Transitional lead from V3-V5, in which transition in V4 accounts for the highest rate of 60.7%. The average QRS time is 136.3 ± 14.4 ms, almost all PVCs have time index of R &lt; 0.5 and amplitude index of R &lt; 0.3… There are 48.3% of patients with only negative component in lead D1; 40% of PVCs have notching at D2, D3, aVF. About cardiac electrophysiolpgy charateristics, for the origin of PVCs, he majority of PVCs in our study originated from the anterior wall of the right ventricular outflow tract with 64.8%; by superior-inferior position, mainly inferior region with 66.9%; according to septum - lateral position, mainly septal region with 72.4%. Most patients had PVCs originated from the inferior-anterior-septal region of the right ventricular outflow tract with 36.6%.

Prognostic impact of morphology and duration of premature ventricular contractions in a population without structural heart disease.

Premature ventricular contractions (PVCs) are a common form of arrhythmia associated with an unfavorable prognosis in patients with structural heart disease. It is unclear whether PVCs site of origin and QRS-width has a prognostic significance in patients without structural heart disease. The aim of this study was to assess the prognostic importance of PVCs morphology and duration in this patient group. We included 511 consecutive patients without a history of previous heart disease. They were examined with echocardiography and exercise test with normal findings. We categorized the PVCs from a 12 lead ECG according to morphology and width of the QRS-complex and analyzed the outcome in terms of a composite endpoint of total mortality and cardiovascular morbidity. During a median follow-up time of 5.3 years, 19 patients (3.5%) died and 61 (11.3%) met the composite outcome. Patients with PVCs originating from the outflow tracts had a significantly lower risk for the composite outcome compared to patients with non-OT-PVCs. Similarly, patients with PVC originating from the right ventricle had a better outcome than patients with left ventricular PCVs. No difference in outcome depending on QRS-width during PVCs was noticed. In our cohort of consecutively included PVC patients without structural heart disease PVCs from the outflow tracts were associated with a better prognostic outcome than non-OT PVCs; the same was true for right ventricular PVCs when compared to left ventricular ones. The classification of the origin of the PVCs was based on 12-lead ECG morphology. QRS-width during PVC did not seem to have prognostic significance.

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data.

Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0mm for the dipole-based model and 28.9-39.2mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

Impact of pacing output and cycle length on QRS morphology in ablation of premature ventricular contractions (PACE-MAP-trial)

Abstract Funding Acknowledgements Type of funding sources: None. Background Pace mapping is an established technique to localize the origin of premature ventricular contractions (PVC). The QRS morphology induced by endocardial pacing is compared with a previously recorded template of the target arrhythmia. Based upon historic in-vitro models pacing at the arrhythmia’s coupling interval (CI) and stimulation threshold has become the gold standard in pace mapping. Purpose There is a paucity of data supporting pacing at the CI and stimulation threshold as the gold standard for pace mapping. In this prospective study, we sought to assess systematically the impact of variations of pacing output and cycle length on the resulting QRS morphology during pace mapping in the context of PVC ablation. Methods All patients undergoing ablation of idiopathic PVC were prospectively enrolled in this study. Pace mapping was performed using four different parameter settings: (1) A predefined fixed burst at maximum output (10V/2ms), (2) fixed burst at stimulation threshold, (3) coupling interval at maximum output and (4) coupling interval at stimulation threshold. Pacing cycle length (PCL) of the fixed burst was defined as follows: For a CI≤450ms = PCL of CI+100ms and for a CI≥450 = PCL of CI-100ms. Using an automated waveform comparison algorithm (Bard® LabSystem™ PRO software; Boston Scientific, MA, USA), the resulting QRS complexes were matched with the clinical PVC, and with the gold standard. For every parameter setting the mean of the matching percentage of three QRS complexes entered the analysis. Results We report data of the first 47 consecutive patients (57±18 years, 64% male) enrolled in this ongoing study between May 2021 and September 2022. The pacing protocols were performed at 75 different sites: 6/75 epicardially via great cardiac vein, 38/75 in the RVOT, 23/75 in LVOT and 8/75 in the left ventricular cavity. When comparing the QRS morphologies resulting from the four different pace mapping settings with the clinical PVC, the univariate analysis revealed no difference between groups (p=0.9). Furthermore, there was no difference comparing the paced QRS morphologies at different output and cycle length to the gold standard of threshold pacing at coupling interval (p=0.9). Conclusion Variations of pacing output and stimulation cycle length during pace mapping does not affect QRS morphology during PVC ablation. Applying a modern automated waveform comparison tool, no advantage was observed when comparing with the gold standard. It is therefore reasonable to assume, that pace mapping can be performed using an easily applicable setting of a fixed burst at maximum pacing output.

PO-05-067 THE USE OF THE HIS BUNDLE LOCAL ACTIVATION TIME IN OUTFLOW TRACT PREMATURE VENTRICULAR CONTRACTIONS TO ASSIST IN LOCALIZATION

Localization of premature ventricular contractions (PVCs) from the outflow tract regions based upon ECG localization criteria may prove challenging based upon cardiac rotation and surface lead placement. Multiple studies describe ECG parameters based upon axis and precordial R wave transition for distinction of left (LVOT) vs right ventricular outflow tract (RVOT) site of origin. No previous studies describe intracardiac fiducial points to assist in site discrimination for PVC origin between LVOT, RVOT, and intramyocardial sites. Assessment of PVC timing at the His bundle region (HB) as a predictor for area of origin of a premature ventricular contraction. Timing at the area of the HB could help discriminate LVOT, RVOT, and intramural PVCs and guide ablation strategy. We performed retrospective analysis of 30 consecutive patients who underwent a PVC ablation from July 2021 until present day. During index mapping, placement of a multipolar catheter in the HB yielded local activation timing (LAT) for PVCs, and the HB was marked on the mapping system . The timing of a PVC was recorded as well as the area of successful ablation. Of the 30 patients 10 were female and 20 male with an average age of 60 years. The average EF was 52%. 10 patients had coronary artery disease 9 had ICDs. 10 Patients with successful LVOT ablation had HB LAT average of -6 msec +/- 12msec. 8 Patients with successful RVOT ablation had HB LAT average of 32 msec +/-11msec. 12 patients with successful intramyocardial ablation had HB LAT of 45 msec +- 8 msec. The difference between LVOT HB LAT and RVOT HB LAT was statically significant P<0.001. The difference between LVOT HB LAT and intramyocardial HB LAT was statistically significant P<0.001. The difference between RVOT HB LAT and intramyocardial HB LAT was statistically significant P<0.0062. Use of the HB as an intracardiac fiducial point for LAT assists with accurate localization and successful ablation of PVCs originating from the outflow tracts.

A "two-step classification" machine learning method for non-invasive localization of premature ventricular contraction origins based on 12-lead ECG.

Premature ventricular contraction (PVC) is a type of cardiac arrhythmia that originates from ectopic pacemaker in the ventricles. The localization of the origin of PVC is essential for successful catheter ablation. However, most studies on non-invasive PVC localization focus on elaborate localization in specific regions of the ventricle. This study aims to propose a machine learning algorithm based on 12-lead electrocardiogram (ECG) data that can improve the accuracy of PVC localization in the whole ventricle. We collected 12-lead ECG data from 249 patients with spontaneous or pacing-induced PVCs. The ventricle was divided into 11 segments. In this paper, we propose a machine learning method consisting of two consecutive classification steps. In the first classification step, each PVC beat was labeled to one of the 11 ventricular segments using six features, including a newly proposed morphological feature called "Peak_index." Four machine learning methods were tested for comparative multi-classification performance and the best classifier result was kept to the next step. In the second classification step, a binary classifier was trained using a smaller combination of features to further differentiate segments that are easily confused. The Peak_index as a proposed new classification feature combined with other features is suitable for whole ventricle classification by machine learning methods. The test accuracy of the first classification reached 75.87%. It is shown that a second classification for confusable categories can improve the classification results. After the second classification, the test accuracy reached 76.84%, and when a sample classified into adjacent segments was considered correct, the test "rank accuracy" was improved to 93.49%. The binary classification corrected 10% of the confused samples. This paper proposes a "two-step classification" method to localize the origin of PVC beats into the 11 regions of the ventricle using non-invasive 12-lead ECG. It is expected to be a promising technique to be used in clinical settings to help guide ablation procedures.

Initial negative concordance on unipolar and bipolar electrograms: a novel parameter for localizing the origin of premature ventricular contractions arising from pulmonary sinus cusps.

The features of the unipolar electrogram (UEGM) and bipolar electrogram (BEGM) have been utilized to identify the site of origin of idiopathic premature ventricular contractions (PVCs) arising from pulmonary sinus cusps (PSCs), but for these PVCs, whether a negative concordance in the initial waves of both EGMs recorded above pulmonary valves can be used as a parameter to localize the origin has not been previously studied. We aimed to assess whether an initial negative concordance (INC) between the UEGM and BEGM might determine the origin of PVCs mapped and ablated within PSCs. Data were collected from 22 patients undergoing successful radiofrequency catheter ablation for symptomatic idiopathic PVCs within PSCs. The morphological features of both the UEGM and the BEGM recorded at all ablation sites were analyzed. A total of 109 sites within PSCs were ablated in 22 patients with an age (mean ± SD) of 47.2 ± 17.2years. Ablation resulted in procedural success in all patients. The INC was observed at 18 of 22 (81.8%) successful ablation sites, contrasted with 3 of 87 (3.4%) unsuccessful sites (P < 0.001). The INC was consistent with the outcomes of conventional mapping parameters and proved to be an additional useful predictor of ablation success, with a sensitivity, specificity, positive predictive value and negative predictive value of 81.8%, 96.6%, 85.7% and 95.5%, respectively. An INC between the UEGM and the BEGM can predict the origin of PVCs arising from PSCs. An initial negative concordance between unipolar and bipolar electrograms indicates that the distal electrode of the ablation catheter is at the origin of premature ventricular contractions within pulmonary sinus cusps.

The characteristics of a 12-lead electrocardiogram measuring premature ventricular contractions originating from the tricuspid annulus using the clock position method.

This study aimed to analyze the conventional surface electrocardiogram (ECG) characteristics of premature ventricular contractions (PVCs) originating from the tricuspid annulus and to investigate the efficacy of locating their origins according to ECG results. Eight patients who underwent radiofrequency ablation in the First Hospital of Shanxi Medical University (China) were included in the study. Pace mapping (PM) was used to analyze the characteristics of the PVCs originating from the tricuspid annulus recorded via 12-lead body surface ECGs. An R-wave was found in leads I, V5 , and V6 . The QRS wave was narrower when the PVCs originated from the septum and shifted in lead V3 (R-wave amplitude/S-wave amplitude in the precordial lead-1). The QRS wave was broadest when the PVCs originated from the 7 to 9 o'clock position. The augmented vector left lead showed RS, QS, or RSR-type waves with a low amplitude when the PVCs originated from the upper part of the annulus. When the PVCs originated from the lower part of the annulus, the augmented vector right lead reflected multidirectional and QS-type waves. The ECG-lead characteristics related to the origin of PVCs in the tricuspid annulus indicate some level of significance and can be used to formulate a specific diagnosis.

1013 RADIOFREQUENCY CATHETER ABLATION OF IDIOPATHIC VENTRICULAR FIBRILLATION: A CURRENT SINGLE-CENTRE EXPERIENCE

Abstract Short-coupled idiopathic ventricular fibrillation (SC-IVF) is a rare, life-threatening arrhythmia, accountable for 5–10% of out-of-hospital cardiac arrests (OHCA). Catheter ablation of the short-coupled premature ventricular contraction (PVC) that triggers VF has been shown to prevent VF recurrences in outdated case series. Aim To evaluate the clinical outcome and summarize the recent experience of 3D electrophysiological mapping and radiofrequency catheter ablation (RFCA) of SC-IVFs in a tertiary high-volume referral centre. Methods from January 2016, we enrolled all consecutive patients diagnosed with SC-IVF and treated by RFCA. Structural heart disease was excluded in all patients by means of echocardiography, cardiac magnetic resonance, coronary angiography, and exercise test. Brugada syndrome and long QT syndrome were excluded by Ajmaline and Epinephrine tests, respectively. The CARTO system was used to construct detailed 3D electroanatomic maps of the right and left ventricle. Mapping and ablation were performed with a standard 3.5- irrigated tip catheter. The PVCs were localized by mapping the earliest bipolar electrogram relative to the onset of the ectopic surface QRS (confirmed by QS complex in the unipolar configuration). Pace mapping was used in patients with infrequent PVCs. The origin of PVCs from fascicles/Purkinje network was indicated by initial sharp potentials preceding the ectopic QRS complex. The procedural end point was the abolition of all clinical PVCs. Outcome (freedom from SC-PVCs and VF episodes) was assessed by Holter monitoring and defibrillator memory interrogation. Results eleven consecutive patients [8 men, 3 women; median age 41 (± 5 years)] were enrolled. 10/11 patients (91%) were asymptomatic prior the index VF event, while 1 patient (9%) experienced recurrent syncope. An aborted OHCA was the index event in all patients. Arrhythmic storm was observed at the index presentation in 2 patients (18%). No patient had family history of sudden cardiac death. All patients were implanted with an ICD after the index event. The delay in SC-IVF diagnosis was 3 (± 2 years) from the index event. 2 patients (18%) had ≥ 2 SC-PVC morphologies. The mean coupling interval (CI) of the SC-PVCs ranged from 230 to 330 msec (mean 303 ± 26 msec). The CI/QT ratio ranged from 0.6 to 1 (mean 0.81 ± 0.15). 10 patients underwent RFCA for recurrent VF/ICD shocks and 1 patient for refractory electrical storm. RFCA was based on activation mapping, pace-mapping, and both in 1 (9%), 1 (9%), and 9 (82%) patients, respectively. The sites of ablation were LV Purkinje, RV Purkinje, RV non-Purkinje (RV outflow tract and tricuspid annulus), and LV non-Purkinje (LV Summit) in 3 (27%), 4 (36%), 3 (27%), and 1 (9%) patient, respectively. The first ablation was acutely successful in all patients. 1 patient experienced recurrence of SC-PVCs and VF episodes after 5 days from the first ablation and underwent a second RFCA. There were no peri-procedural complications. After a median follow-up of 22 months (ranging from 6 to 39 months) all patients were free from sustained ventricular arrhythmia recurrences: 10 patients (91%) were free from VF and PVCs; 1 patient (9%) had recurrent SC-PVCs without VF episodes. Conclusions this retrospective study explored the effectiveness of RFCA combined with the advanced electroanatomic mapping in a current cohort of patients with history of OHCA diagnosed with SC-IVF. RFCA of SC-PVCs was safe and highly effective in abolishing VF episodes in all patients.

Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features.

Precise localization of the site of origin of premature ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure. The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ventricular outflow tract and right ventricular outflow tract (RVOT) using surface electrocardiogram characteristics. A total of 851 patients undergoing radiofrequency ablation of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features. In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accuracy 94.23%; sensitivity 97.10%; specificity 88.57%). In the prospective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%). Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.