Lead I R-wave amplitude to distinguish ventricular arrhythmias with lead V3 transition originating from the left versus right ventricular outflow tract

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

PURPOSE

The 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).

METHODS

A 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.

RESULTS

The 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.

CONCLUSION

Lead 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).

A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins

INTRODUCTION

Diagnosis of outflow tract ventricular arrhythmia (OTVA) localization by an electrocardiographic complex is key to successful catheter ablation for OTVA. However, diagnosing the origin of OTVA with a precordial transition in lead V3 (V3TZ) is challenging. This study aimed to create the best practical electrocardiogram algorithm to differentiate the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using machine learning.

METHODS

Of 498 consecutive patients undergoing catheter ablation for OTVA, we included 104 patients who underwent ablation for OTVA with V3TZ and identified the origin of LVOT (n = 62) and RVOT (n = 42) from the results. We analyzed the standard 12-lead electrocardiogram preoperatively and measured 128 elements in each case. The study population was randomly divided into training group (70%) and testing group (30%), and decision tree analysis was performed using the measured elements as features. The performance of the algorithm created in the training group was verified in the testing group.

RESULTS

Four measurements were identified as important features: the aVF/II R-wave ratio, the V2S/V3R index, the QRS amplitude in lead V3, and the R-wave deflection slope in lead V3. Among them, the aVF/II R-wave ratio and the V2S/V3R index had a particularly strong influence on the algorithm. The performance of this algorithm was extremely high, with an accuracy of 94.4%, precision of 91.5%, recall of 100%, and an F1-score of 0.96.

CONCLUSIONS

The novel algorithm created using machine learning is useful in diagnosing the origin of OTVA with V3TZ.

ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images

BACKGROUND

Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects.

METHODS

We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet.

RESULTS

Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process.

CONCLUSION

The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.

Lead I R-wave amplitude to distinguish ventricular arrhythmias with lead V3 transition originating from the left versus right ventricular outflow tract

BACKGROUND

The electrophysiology algorithm for localizing left or right origins of outflow tract ventricular arrhythmias (OT-VAs) with lead V₃ transition still needs further investigation in clinical practice.

HYPOTHESIS

Lead I R-wave amplitude is effective in distinguishing the left or right origin of OT-VAs with lead V₃ transition.

METHODS

We measured lead I R-wave amplitude in 82 OT-VA patients with lead V₃ transition and a positive complex in lead I who underwent successful catheter ablation from the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT). The optimal R-wave threshold was identified, compared with the V₂ S/V₃ R index, transitional zone (TZ) index, and V₂ transition ratio, and validated in a prospective cohort study.

RESULTS

Lead I R-wave amplitude for LVOT origins was significantly higher than that for RVOT origins (0.55 ± 0.13 vs. 0.32 ± 0.15 mV; p < .001). The area under the curve (AUC) for lead I R-wave amplitude as assessed by receiver operating characteristic (ROC) analysis was 0.926, with a cutoff value of ≥0.45 predicting LVOT origin with 92.9% sensitivity and 88.2% specificity, superior to the V₂ S/V₃ R index, TZ index, and V₂ transition ratio. VAs in the LVOT group mainly originated from the right coronary cusp (RCC) and left and right coronary cusp junction (L-RCC). In the prospective study, lead I R-wave amplitude identified the LVOT origin with 92.3% accuracy.

CONCLUSION

Lead I R-wave amplitude provides a useful and simple criterion to identify RCC or L-RCC origin in OT-VAs with lead V₃ transition.