Automatic Coordinate Prediction of the Exit of Ventricular Tachycardia from 12-Lead Electrocardiogram

Gyawali, Prashnna Kumar; Chen, Shuhang; Liu, Huafeng; Horáček, B. Milan; Sapp, John L.; Wang, Linwei

doi:10.22489/cinc.2017.041-280

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…here the exit site of the VT circuit) directly from multi-channel surface ECG data recorded during VT, and even potentially from the standard 12-lead ECG recording [24]. Alternatively, artificial intelligence has shown promising results to predict the exit site of the VT circuit from the 12-lead ECG of the VT, based on machine learning [25] or deep learning [26]. In particular, in Ref.…”

Section: Discussionmentioning

confidence: 99%

Catheter Treatment of Ventricular Tachycardia: A Reference-Less Pace-Mapping Method to Identify Ablation Targets

Odille

Battaglia

Hoyland

et al. 2019

IEEE Trans. Biomed. Eng.

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A novel method is developed to identify ablation targets for the catheter treatment of ventricular tachycardia (VT). Methods: The method is based on pacemapping, which is a validated technique to determine the catheter ablation targets. Conventionally, it consists of stimulating the heart ventricle from various sites and comparing the resulting activation pathways to that of a clinical VT, by analysis of surface electrocardiograms (ECG). In this work a novel pace-mapping method is presented, that does not require a reference ECG recording of the VT. A 3D correlation gradient map is reconstructed by semi-automatic analysis of ECG morphological changes within the network of pace-mapping sites. In these maps, abnormal points are identified by high correlation gradient values (i.e. corresponding to slow propagation of the electric influx, as in the core of the reentrant VT circuit). The relation between the conventional and reference-less method is described theoretically and evaluated in a retrospective study including 24 VT ablation procedures. Results: The "reference-less" method was able to identify normal points with a high accuracy (negative predictive value: NPV=97%) and to detect more abnormal points, as predicted by the theory. Correlation gradients computed by the proposed method were significantly higher in ablation zones than in other zones of the ventricle (p<10-12), indicating excellent prediction of the ablation targets. Significance: The reference-less method might either be used in complement of the conventional method, or to treat patients in whom VT cannot be induced during the intervention.

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Section: Discussionmentioning

confidence: 99%

Catheter Treatment of Ventricular Tachycardia: A Reference-Less Pace-Mapping Method to Identify Ablation Targets

Odille

Battaglia

Hoyland

et al. 2019

IEEE Trans. Biomed. Eng.

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show abstract

“…Localization and annotation of specific waves in ECG signals are of great importance to cardiologists in order to help them in diagnosing cardiac diseases such as atrial fibrillation. For instance, AE [51] and RNN [52] are employed to automatically localize the exit of ventricular tachycardia from the 12-lead electrocardiogram. Some studies focused instead on the localization of origins of premature ventricular [203], as well as for the Myocardial Infarction [216].…”

Section: Localization or Annotationmentioning

confidence: 99%

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

Hong¹,

Zhou²,

Shang³

et al. 2020

Preprint

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Background: Electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results predictive healthcare tasks using ECG signals. Objective: This paper conducts a systematic review of deep learning methods on ECG data from both model and application perspectives. Methods: We extracted papers that deploy deep learning (deep neural networks) models on ECG data that published between January 1st 2010 and February 29th 2020 from Google Scholar, PubMed, and DBLP. We then analyze them in three aspects, including task, model, and data. Last we discuss open challenges and unsolved problems in this area. Results: The total number of papers is 191; among them, 108 papers are published after the year 2019. Almost all kinds of common deep learning architectures have been used in ECG analytics tasks like disease detection/classification, annotation/localization, sleep staging, biometric human identification, denoising, and so on. Conclusion:The number of works about deep learning on Electrocardiogram data is growing explosively in recent years. Indeed, these works have achieved a far better performance in terms of accuracy. However, there are some new challenges and problems like interpretability, scalability, efficiency, which need to be addressed and paid more attention. Moreover, it is also worth investigating by discovering new interesting applications from both the dataset view and the method view. Significance: This paper summarizes existing deep learning methods on modeling ECG data from multiple views while also point out existing challenges and problems while it can become a potential research direction in the future.

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

Wang

Feng

Zhong

et al. 2023

J Interv Card Electrophysiol

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Automatic Coordinate Prediction of the Exit of Ventricular Tachycardia from 12-Lead Electrocardiogram

Cited by 6 publications

References 7 publications

Catheter Treatment of Ventricular Tachycardia: A Reference-Less Pace-Mapping Method to Identify Ablation Targets

Catheter Treatment of Ventricular Tachycardia: A Reference-Less Pace-Mapping Method to Identify Ablation Targets

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

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

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