ArticleIEEE transactions on bio-medical engineering2018
Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.Cardiovascular engineering and technology · 2026Review
- Advancements in artificial intelligence for the localization of premature ventricular contraction origins.Frontiers in cardiovascular medicine · 2026Review
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- A Deep Learning Approach Toward Differentiating Left versus Right for Idiopathic Ventricular Arrhythmia Originated from Outflow Tract.Journal of medical signals and sensors · 2025Article
- BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping.Computers in biology and medicine · 2024Article
- A "two-step classification" machine learning method for non-invasive localization of premature ventricular contraction origins based on 12-lead ECG.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024Article
- Improving localization accuracy for non-invasive automated early left ventricular origin localization approach.Frontiers in physiology · 2023Article
- Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning.IEEE journal of translational engineering in health and medicine · 2023Article
- Big Data in electrophysiology.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.Frontiers in physiology · 2022Article
- Machine Learning in Arrhythmia and Electrophysiology.Circulation research · 2021Review
- Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis.Frontiers in physiology · 2021Article
- A Novel Model Based on Spatial and Morphological Domains to Predict the Origin of Premature Ventricular Contraction.Frontiers in physiology · 2021Article
- Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach.Frontiers in physiology · 2021Article
- A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms.Computers in biology and medicine · 2020Article
- Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms.Scientific reports · 2020Article
- Prospective Assessment of an Automated Intraprocedural 12-Lead ECG-Based System for Localization of Early Left Ventricular Activation.Circulation. Arrhythmia and electrophysiology · 2020Article
- Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms.IEEE transactions on bio-medical engineering · 2020Article
- Application of Neural Networks to 12-Lead Electrocardiography - Current Status and Future Directions.Circulation reports · 2019Review
- Deep Learning on 1-D Biosignals: a Taxonomy-based Survey.Yearbook of medical informatics · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThis paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model.
methodsThe proposed method consists of two CNNs (Segment CNN and Epi-Endo CNN) to classify among ventricular sources from 25 segments and from epicardium (Epi) or endocardium (Endo). The inputs are the full time courses and the first half of QRS complexes of 12-lead ECG, respectively. After registering the ventricle computer model with an individual patient's heart, the training datasets were generated by multiplying ventricular current dipoles derived from single pacing at various locations with patient-specific lead field. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. A number of computer simulations were conducted to evaluate the proposed method under a variety of noise levels and heart registration errors. Furthermore, the proposed method was evaluated on 90 PVC beats from nine human patients with PVCs and compared against ablation outcome in the same patients.
resultsThe computer simulation evaluation returned relatively high accuracies for Segment CNN (∼78%) and Epi-Endo CNN (∼90%). Clinical testing in nine PVC patients resulted an averaged localization error of 11 mm.
conclusionOur simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN-based method for localization of PVC. SIGNIFICANCE: This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.
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