scholarly journals Pattern Matching Filter and multielectrode mapping catheter – A new approach for complex premature ventricular contraction ablation

2021 ◽  
Vol 40 (6) ◽  
pp. 423-431
Author(s):  
Pedro A. Sousa ◽  
Natália António ◽  
Sérgio Barra ◽  
Luís Elvas ◽  
Lino Gonçalves
Keyword(s):  
Pattern Matching ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
New Approach ◽  
Matching Filter ◽  
Mapping Catheter

scholarly journals Multielectrode Mapping and Pattern Matching Recognition for Left Premature Ventricular Contraction Ablation

2019 ◽  
Vol 5 (4) ◽  
pp. 528-529
Author(s):  
Pedro A. Sousa ◽  
Luís Elvas ◽  
Sérgio Barra ◽  
Natália António ◽  
Lino Gonçalves
Keyword(s):  
Pattern Matching ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction

scholarly journals CARDIAC AFFERENT SIGNALING PARTIALLY UNDERLIES PREMATURE VENTRICULAR CONTRACTION-INDUCED CARDIOMYOPATHY

2021 ◽  
Vol 77 (18) ◽  
pp. 579
Author(s):  
Yuichi Hori ◽  
Taro Temma ◽  
Christian Wooten ◽  
Christopher O Sobowale ◽  
Christopher Chan ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Afferent Signaling

scholarly journals Robust deep learning pipeline for PVC beats localization

2021 ◽  
Vol 29 ◽  
pp. 475-486
Author(s):  
Bohdan Petryshak ◽  
Illia Kachko ◽  
Mykola Maksymenko ◽  
Oles Dobosevych
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Ecg Signal ◽  
Ventricular Contraction ◽  
Localization Task ◽  
Robust Performance ◽  
Extensive Evaluation ◽  
Decoder Architecture ◽  
Public Datasets

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

scholarly journals B-PO03-123 RELATION BETWEEN PREMATURE VENTRICULAR CONTRACTION ORIGIN, UNDERLYING HEART DISEASE AND ABLATION OUTCOME

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S238-S239
Author(s):  
Kenichi Tokutake ◽  
Ikutaro Nakajima ◽  
Ansel P. Amaral ◽  
Jason Cook ◽  
Asad A. Aboud ◽  
...  
Keyword(s):  
Heart Disease ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction

Direct Intramyocardial Ethanol Injection for Premature Ventricular Contraction Arising From the Inaccessible Left Ventricular Summit

2021 ◽  
Author(s):  
Gurukripa N. Kowlgi ◽  
Arman Arghami ◽  
Juan A. Crestanello ◽  
Christopher J. François ◽  
Paul A. Friedman ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Left Ventricular ◽  
Ventricular Contraction ◽  
Ethanol Injection

A low-complexity data-adaptive approach for premature ventricular contraction recognition

2013 ◽  
Vol 8 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Peng Li ◽  
Chengyu Liu ◽  
Xinpei Wang ◽  
Dingchang Zheng ◽  
Yuanyang Li ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Low Complexity ◽  
Ventricular Contraction ◽  
Adaptive Approach ◽  
Data Adaptive

Low-power perceptron model based ECG processor for premature ventricular contraction detection

2018 ◽  
Vol 59 ◽  
pp. 29-36 ◽  
Author(s):  
Zhijian Chen ◽  
Huanzhang Xu ◽  
Jiahui Luo ◽  
Taotao Zhu ◽  
Jianyi Meng
Keyword(s):  
Low Power ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Model Based
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