Usefulness of ventricular endocardial electric reconstruction from body surface potential maps to noninvasively localize ventricular ectopic activity in patients

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG

OBJECTIVE

The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhythmias. Existing methods rely heavily on manual extraction of PVC beats, which limits their application to the automatic PVC recognition from long-term data recorded by ECG monitors before and during operation. In addition, research identifying PVC sources in the whole ventricle have not been reported. The purpose of this study was to validate the feasibility of localization of origins of PVC in the whole ventricle and to explore an automatic algorithm for recognition of PVC beats based on long-term 12-lead ECG.

APPROACH

This study included 249 patients with spontaneous PVCs or pacing-induced PVCs. A novel algorithm was used to automatically extract PVC beats from a massive amount of original ECG data, which was collected by different acquisition devices. After clustering and labelling, 374 sample groups, each containing dozens to hundreds of PVC beats, formed the entire dataset of 11 categories corresponding to 11 regions of PVC origins in the whole ventricle. To choose the best classification model for the current task, four machine learning methods, support vector machine (SVM), random forest (RF), gradient-boosting decision tree (GBDT) and Gaussian naïve Bayes (GNB), were compared by randomly selecting 70% of the entire dataset (sample groups = 257) for training and the remaining 30% (sample groups = 117) for testing. The average performance of each model was estimated by the bootstrap method using 1000 resampling trials.

MAIN RESULTS

For PVC beat recognition, the achieved testing accuracy, sensitivity and specificity is 97.6%, 98.3% and 96.7%, respectively. For localization purpose, the achieved testing accuracy varies slightly from 70.7% to 74.1% among four classifiers, and when neighboring regions were combined, the testing rank accuracy is improved to a range of 91.5% to 93.2%.

SIGNIFICANCE

The proposed algorithm can automatically recognize PVC beats and map them to one of the 11 regions in the whole ventricle. Owing to the high accuracy of PVC beat recognition and the capability to target the potential PVC origins in multi regions, it is expected to be a predominant technique being used in clinical settings to automatically analyze huge ECG data before and during operation so as to replace the tedious manual identification.

Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms

Building a data-driven model to localize the origin of ventricular activation from 12-lead electrocardiograms (ECG) requires addressing the challenge of large anatomical and physiological variations across individuals. The alternative of a patient-specific model is, however, difficult to implement in clinical practice because the training data must be obtained through invasive procedures. In this paper, we present a novel approach that overcomes this problem of the scarcity of clinical data by transferring the knowledge from a large set of patient-specific simulation data while utilizing domain adaptation to address the discrepancy between the simulation and clinical data. The method that we have developed quantifies non-uniformly distributed simulation errors, which are then incorporated into the process of domain adaptation in the context of both classification and regression. This yields a quantitative model that, with the addition of 12-lead ECG data from each patient, provides progressively improved patient-specific localizations of the origin of ventricular activation. We evaluated the performance of the presented method in localizing 75 pacing sites on three in-vivo premature ventricular contraction (PVC) patients. We found that the presented model showed an improvement in localization accuracy relative to a model trained on clinical ECG data alone or a model trained on combined simulation and clinical data without considering domain shift. Furthermore, we demonstrated the ability of the presented model to improve the real-time prediction of the origin of ventricular activation with each added clinical ECG data, progressively guiding the clinician towards the target site.

Protective effect of diethylcarbamazine inhibits NF-κB activation in isoproterenol-induced acute myocardial infarction rat model through the PARP pathway

The present study investigated the protective effect of diethylcarbamazine in inhibiting nuclear factor (NF)-κB activation in isoproterenol‑induced acute myocardial infarction (AMI) rats through the poly ADP ribose polymerase (PARP) pathway. Male albino Wistar rats were injected subcutaneously with isoproterenol (100 mg/kg/day) for 2 days to induce an AMI model. Diethylcarbamazine (50 mg/kg) was administered by gavage for 12 days prior to the isoproterenol-induced AMI. It was noted that diethylcarbamazine significantly inhibited AMI‑induced casein kinase and lactate dehydrogenase levels, and reduced the AMI‑induced wet heart weight to body weight ratio in AMI rats. Diethylcarbamazine treatment significantly weakened reactive oxygen species production and reduced the levels of tumor necrosis factor (TNF)‑α, interleukin‑6 and NF‑κB/p65 in AMI rats. Western blotting demonstrated that diethylcarbamazine significantly suppressed the AMI‑induced inducible nitric oxide synthase (iNOS), transforming growth factor (TGF)‑β1, cyclooxygenase‑2 (COX‑2) and PARP protein expression in AMI rats. The results demonstrated that the protective effect of diethylcarbamazine inhibited isoproterenol‑induced AMI through the suppression of inflammation, iNOS, TGF‑β1, COX‑2 and the PARP pathway, and revealed the clinical potential of diethylcarbamazine for therapeutic and clinical applications.

ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone

ECG imaging is an emerging technology for the reconstruction of cardiac electric activity from non-invasively measured body surface potential maps. In this case report, we present the first evaluation of transmurally imaged activation times against endocardially reconstructed isochrones for a case of sustained monomorphic ventricular tachycardia (VT). Computer models of the thorax and whole heart were produced from MR images. A recently published approach was applied to facilitate electrode localization in the catheter laboratory, which allows for the acquisition of body surface potential maps while performing non-contact mapping for the reconstruction of local activation times. ECG imaging was then realized using Tikhonov regularization with spatio-temporal smoothing as proposed by Huiskamp and Greensite and further with the spline-based approach by Erem et al. Activation times were computed from transmurally reconstructed transmembrane voltages. The results showed good qualitative agreement between the non-invasively and invasively reconstructed activation times. Also, low amplitudes in the imaged transmembrane voltages were found to correlate with volumes of scar and grey zone in delayed gadolinium enhancement cardiac MR. The study underlines the ability of ECG imaging to produce activation times of ventricular electric activity-and to represent effects of scar tissue in the imaged transmembrane voltages.

Non-invasive cardiac mapping in clinical practice: Application to the ablation of cardiac arrhythmias

Ten years ago, electrocardiographic imaging (ECGI) started to demonstrate its efficiency in clinical settings. The initial application to localize focal ventricular arrhythmias such as ventricular premature beats was probably the easiest to challenge and validates the concept. Our clinical experience in using this non-invasive mapping technique to identify the sources of electrical disorders and guide catheter ablation of atrial arrhythmias (premature atrial beat, atrial tachycardia, atrial fibrillation), ventricular arrhythmias (premature ventricular beats) and ventricular pre-excitation (Wolff-Parkinson-White syndrome) is described here.

Contemporary Mapping Techniques of Complex Cardiac Arrhythmias - Identifying and Modifying the Arrhythmogenic Substrate

Cardiac electrophysiology has moved a long way forward during recent decades in the comprehension and treatment of complex cardiac arrhythmias. Contemporary electroanatomical mapping systems, along with state-of-the-art technology in the manufacture of electrophysiology catheters and cardiac imaging modalities, have significantly enriched our armamentarium, enabling the implementation of various mapping strategies and techniques in electrophysiology procedures. Beyond conventional mapping strategies, ablation of complex fractionated electrograms and rotor ablation in atrial fibrillation ablation procedures, the identification and modification of the underlying arrhythmogenic substrate has emerged as a strategy that leads to improved outcomes. Arrhythmogenic substrate modification also has a major role in ventricular tachycardia ablation procedures. Optimisation of contact between tissue and catheter and image integration are a further step forward to augment our precision and effectiveness. Hybridisation of existing technologies with a reasonable cost should be our goal over the next few years.

Noninvasive mapping of ventricular arrhythmias

Several decades of research has led to the development of a 252-lead electrocardiogram-based three-dimensional imaging modality to refine noninvasive diagnosis and improve the management of heart rhythm disorders. This article reviews the clinical potential of this noninvasive mapping technique in identifying the sources of electrical disorders and guiding the catheter ablation of ventricular arrhythmias (premature ventricular beats and ventricular tachycardia). The article also briefly refers to the noninvasive electrical imaging of the arrhythmogenic ventricular substrate based on the electrophysiologic characteristics of postinfarction ventricular myocardium.

Investigations of sensitivity and resolution of ECG and MCG in a realistically shaped thorax model

Solving the inverse problem of electrocardiography (ECG) and magnetocardiography (MCG) is often referred to as cardiac source imaging. Spatial properties of ECG and MCG as imaging systems are, however, not well known. In this modelling study, we investigate the sensitivity and point-spread function (PSF) of ECG, MCG, and combined ECG+MCG as a function of source position and orientation, globally around the ventricles: signal topographies are modelled using a realistically-shaped volume conductor model, and the inverse problem is solved using a distributed source model and linear source estimation with minimal use of prior information. The results show that the sensitivity depends not only on the modality but also on the location and orientation of the source and that the sensitivity distribution is clearly reflected in the PSF. MCG can better characterize tangential anterior sources (with respect to the heart surface), while ECG excels with normally-oriented and posterior sources. Compared to either modality used alone, the sensitivity of combined ECG+MCG is less dependent on source orientation per source location, leading to better source estimates. Thus, for maximal sensitivity and optimal source estimation, the electric and magnetic measurements should be combined.

Non Invasive ECG Mapping To Guide Catheter Ablation

Since more than 100 years, 12-lead electrocardiography (ECG) is the standard-of-care tool, which involves measuring electrical potentials from limited sites on the body surface to diagnose cardiac disorder, its possible mechanism and the likely site of origin. Several decades of research has led to the development of a 252-lead-ECG and CT-scan based, three dimensional, electro-imaging modality to non-invasively map abnormal cardiac rhythms including fibrillation. These maps provide guidance towards ablative therapy and thereby help advance the management of complex heart rhythm disorders. Here, we describe the clinical experience obtained using non-invasive technique in mapping the electrical disorder and guide the catheter ablation of atrial arrhythmias (premature atrial beat, atrial tachycardia, atrial fibrillation), ventricular arrhythmias (premature ventricular beats) and ventricular pre-excitation (Wolff-Parkinson-White syndrome).

Noninvasive imaging of cardiac excitation: current status and future perspective

Noninvasive imaging of cardiac excitation using body surface potential mapping (BSPM) data and inverse procedures is an emerging technique that enables estimation of myocardial depolarization and repolarization. Despite numerous reports on the possible advantages of this imaging technique, it has not yet advanced into daily clinical practice. This is mainly due to the time consuming nature of data acquisition and the complexity of the mathematics underlying the used inverse procedures. However, the popularity of this field of research has increased and noninvasive imaging of cardiac electrophysiology is considered a promising tool to complement conventional invasive electrophysiological studies. Furthermore, the use of appropriately designed electrode vests and more advanced computers has greatly reduced the procedural time. This review provides descriptive overview of the research performed thus far and the possible future directions. The general challenges in routine application of BSPM and inverse procedures are discussed. In addition, individual properties of the biophysical models underlying the inverse procedures are illustrated.