In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations

Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation

Introduction

Extracting beat-by-beat information from electrocardiograms (ECGs) is crucial for various downstream diagnostic tasks that rely on ECG-based measurements. However, these measurements can be expensive and time-consuming to produce, especially for long-term recordings. Traditional ECG detection and delineation methods, relying on classical signal processing algorithms such as those based on wavelet transforms, produce high-quality delineations but struggle to generalise to diverse ECG patterns. Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. However, supervised ML techniques require large annotated datasets for training, and existing datasets for ECG detection/delineation are limited in size and the range of pathological conditions they represent.

Methods

This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from "pools" of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples.

Results

The proposed approach achieves remarkable performance, with a F 1 -score of 99.38% and delineation errors of 2.19 ± 17.73  ms and 4.45 ± 18.32  ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches.

Discussion

Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

A novel ECG algorithm to differentiate between ventricular arrhythmia from right versus left ventricular outflow tract

AIM

The aim of this study was to evaluate the accuracy of the diagnostic criteria for determining the origin of outflow tract ventricular arrhythmia (OTVA) and develop an ECG algorithm to predict its origin.

METHOD

We analyzed the ECGs of 100 patients with OTVA who underwent successful ablation. The QRS complex was measured during sinus rhythm and ventricular arrhythmia. After the ECG algorithm was developed, it was validated in an additional 100 patients from two different hospitals.

RESULTS

In this retrospective study, among the parameters without restrictions in the transition lead, the V2S/V3R index (AUC = 0.96) was significantly better in predicting ventricular arrhythmia originating from the right ventricular outflow tract (RVOT). Further, the larger initial r wave surface area (ISA) in V1 and V2 (AUC = 0.06) was significantly better in predicting ventricular arrhythmias originating from the left ventricular outflow tract (LVOT). Among the parameters with the transition lead in V3, the V2S/V3R index (AUC = 0.82) was significantly better in predicting VAs originating from the RVOT. On the contrary, the V3 R-wave deflection interval (AUC = 0.19) was significantly better in predicting ventricular arrhythmias originating from the LVOT. The algorithm combining the V2S/V3R index and the larger ISA in V1 and V2 could predict OTVA origin with an accuracy of 95.00%, a sensitivity of 87.18%, a specificity of 100.00%, a positive predictive value (PPV) of 100.00%, and a negative predictive value (NPV) of 92.42%. In the validation study, the algorithm exhibited excellent accuracy (95.00%) and AUC (AUC = 0.95), with a sensitivity of 94.12%, a specificity of 95.45%, a PPV of 91.43%, and an NPV of 96.92%.

CONCLUSION

Our developed algorithm can reliably predict OTVA origin without restrictions in the transition lead.

25 years of basic and translational science in EP Europace: novel insights into arrhythmia mechanisms and therapeutic strategies

In the last 25 years, EP Europace has published more than 300 basic and translational science articles covering different arrhythmia types (ranging from atrial fibrillation to ventricular tachyarrhythmias), different diseases predisposing to arrhythmia formation (such as genetic arrhythmia disorders and heart failure), and different interventional and pharmacological anti-arrhythmic treatment strategies (ranging from pacing and defibrillation to different ablation approaches and novel drug-therapies). These studies have been conducted in cellular models, small and large animal models, and in the last couple of years increasingly in silico using computational approaches. In sum, these articles have contributed substantially to our pathophysiological understanding of arrhythmia mechanisms and treatment options; many of which have made their way into clinical applications. This review discusses a representative selection of EP Europace manuscripts covering the topics of pacing and ablation, atrial fibrillation, heart failure and pro-arrhythmic ventricular remodelling, ion channel (dys)function and pharmacology, inherited arrhythmia syndromes, and arrhythmogenic cardiomyopathies, highlighting some of the advances of the past 25 years. Given the increasingly recognized complexity and multidisciplinary nature of arrhythmogenesis and continued technological developments, basic and translational electrophysiological research is key advancing the field. EP Europace aims to further increase its contribution to the discovery of arrhythmia mechanisms and the implementation of mechanism-based precision therapy approaches in arrhythmia management.

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 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0 mm for the dipole-based model and 28.9-39.2 mm 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).

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.