Premature Ventricular Contraction from Right-Left Coronary Cusp Commissure

2020 ◽  
pp. 401-404
Author(s):  
Luis Saenz
Keyword(s):  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Coronary Cusp

scholarly journals Evaluation of Morphology of Premature Ventricular Contraction on 12-Lead Electrocardiogram

2017 ◽  
Vol 31 (2) ◽  
pp. 74-79
Author(s):  
Umme Habiba Ferdaushi ◽  
M Atahar Ali ◽  
Shaila Nabi ◽  
Mainul Islam ◽  
Md Shamshul Alam ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Wave Pattern ◽  
Ventricular Contraction ◽  
Myocardial Diseases ◽  
Qrs Morphology ◽  
Qrs Duration ◽  
Coronary Cusp ◽  
Procedural Planning ◽  
The Right ◽  
Right Coronary Cusp

Background-Evaluation of different morphology of premature ventricular contraction (PVC) in 12-lead ECG might reflect the presence or absence of myocardial diseases and determine PVC foci. It is important for ablation procedure and it can help in pre-procedural planning and potentially may improve ablation outcome.Methods and Results-In this study, 12-lead Electrocardiogram (ECG) of 50 patients with or without structural cardiac diseases, who had experienced PVC, were obtained. PVC QRS duration, contour, pattern, unifocal or multifocaland different morphology in various lead were evaluated. PVC-QRS morphology of 50 ECGs showed QRSd d” 140ms was 60%, >140ms was 24%, >160ms was 16%. QRS notching <40ms was 42%, >40ms was 16%, smooth contour was 42%. The morphology of PVCs in lead V1, RBBB morphology was 36%, LBBB morphology was 64%; in lead V1 & V2, high r 8%, low r 4%. QRS wave polarity in lead I negative (QS, Qr, or rS wave pattern) 28%, positive (R-wave pattern) 52%; in lead II, III, avF, positive 76%. Of these RR’ or Rr’ pattern 20%, R pattern 56%. Negative 24%. QRS transition in chest lead, 16% transition occur at V4 –V5, 48% at V3-V4, 4% at V2-V3, 36% at V1-V2 level. The pattern of PVCs were bigeminy 24%, trigeminy 6%, couplet 4%, salvos 12%, R on T 2%, VT 6%. Of the 32 PVCs originating from the RVOT, 8 were classified as of free-wall origin, 24 of septal, 14 of left, 26 of right, 4 of proximal, and 2 of distal origin. Of the 6 PVCs originating from the LVOT, 4 were originated from the LVOT close to the left coronary cusp and 2 were originated from the LVOT close to the right coronary cusp. Of the 12 PVCs originated from LV fascicle, 12 of posterior fascicle origin and none from anterior fascicle origin.Conclusion-12-lead ECG is a simple, inexpensive and noninvasive tool to detect PVCs and facilitate their localization. By evaluating morphology of PVC, we can also predict the structural and functional state of heart.Bangladesh Heart Journal 2016; 31(2) : 75-79

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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