Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease

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).

Strain Imaging and Ventricular Arrhythmia

Ventricular arrhythmia is one of the main causes of sudden cardiac death. Hence, identifying patients at risk of ventricular arrhythmias and sudden cardiac death is important but can be challenging. The indication for an implantable cardioverter defibrillator as a primary preventive strategy relies on the left ventricular ejection fraction as a measure of systolic function. However, ejection fraction is flawed by technical constraints and is an indirect measure of systolic function. There has, therefore, been an incentive to identify other markers to optimize the risk prediction of malignant arrhythmias to select proper candidates who could benefit from an implantable cardioverter defibrillator. Speckle-tracking echocardiography allows for a detailed assessment of cardiac mechanics, and strain imaging has repeatedly been shown to be a sensitive technique to identify systolic dysfunction unrecognized by ejection fraction. Several strain measures, including global longitudinal strain, regional strain, and mechanical dispersion, have consequently been proposed as potential markers of ventricular arrhythmias. In this review, we will provide an overview of the potential use of different strain measures in the context of ventricular arrhythmias.

Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals

Heart disease should be treated quickly when symptoms appear. Machine-learning methods for detecting heart disease require desktop computers, an obstacle that can have fatal consequences for patients who must check their health periodically. Herein, we propose a MobileNet-based ensemble algorithm for arrhythmia diagnosis that can be easily and quickly operated in a mobile environment. The electrocardiogram (ECG) signal measured over a short period of time was augmented using the matching pursuit algorithm to achieve a high accuracy. The arrhythmia data were classified through an ensemble classifier combining MobileNetV2 and BiLSTM. By classifying the data using this algorithm, an accuracy of 91.7% was achieved. The performance of the algorithm was evaluated using a confusion matrix and a receiver operating characteristic curve. The sensitivity, specificity, precision, and F1 score were 0.92, 0.91, 0.92, and 0.92, respectively. Because the proposed algorithm does not require long-term ECG signal measurement, it facilitates health management for busy people. Moreover, parameters are exchanged when learning data, enhancing the security of the system. In addition, owing to the lightweight deep-learning model, the proposed algorithm can be applied to mobile healthcare, object detection, text recognition, and authentication.

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia. In this study, 158 patients with rapid arrhythmia treated by radiofrequency ablation were divided into effective treatment group (142 cases) and ineffective treatment group (16 cases). ECG examination was performed on all patients, and the indicators of ECG examination were quantified by the deep learning-based convolutional neural network model. The indicators of ECG examination of the effective treatment group and the ineffective treatment group were compared. The results showed that compared with the ineffective treatment group, the end-systolic volume (ESV), end-diastolic volume (EDV), end-systolic volume index (ESVI), and end-diastolic volume index (EDVI) of the effective treatment group were significantly decreased, and the left ventricular ejection fraction (LVEF) was significantly increased (P < 0.05). After radiofrequency ablation, the ventricular rate of patients in the effective treatment group was significantly lower than that of the ineffective treatment group at 12 h and 24 h after treatment (P < 0.05). In addition, compared with patients in the ineffective treatment group, the QT dispersion of the ECG in the effective treatment group was significantly higher (P < 0.05). The accuracy, specificity, and sensitivity of ECG in evaluating the therapeutic effect of patients with tachyarrhythmia were 86.81%, 84.29%, and 77.27%, respectively. The area under the curve was determined as 0.798 according to the receiver operating characteristic (ROC) curve of the subjects. In summary, indicators of ECG examination based on deep learning can provide auxiliary reference information for the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia.