scholarly journals Electrophysiological study of functional bundle branch block: With particular reference to the similarity to premature ventricular contraction and R on T phenomenon

1984 ◽  
Vol 4 (3) ◽  
pp. 309-318
Author(s):  
Ken Okumura ◽  
Yutaka Horio ◽  
Manabu Rokutanda ◽  
Atsumi Hirata ◽  
Hideo Uchida ◽  
...  
Keyword(s):  
Electrophysiological Study ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Bundle Branch Block

scholarly journals Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jeong-Hwan Kim ◽  
Seung-Yeon Seo ◽  
Chul-Gyu Song ◽  
Kyeong-Seop Kim
Keyword(s):  
Neural Network ◽  
Positive Predictive Value ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Neural Network Architecture ◽  
Bundle Branch Block ◽  
Normal Sinus

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contraction, and right/left bundle branch block arrhythmia. Based on testing MIT-BIH arrhythmia benchmark databases, the scope of training/test ECG data was configured by covering at least three and seven R-peak features, and the proposed extended-GoogLeNet architecture can classify five distinct heartbeats; normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle brunch block(LBBB), with an accuracy of 95.94%, an error rate of 4.06%, a maximum sensitivity of 96.9%, and a maximum positive predictive value of 95.7% for judging a normal or an abnormal beat with considering three ECG segments; an accuracy of 98.31%, a sensitivity of 88.75%, a specificity of 99.4%, and a positive predictive value of 94.4% for classifying APC from NSR, PVC, APC beats, whereas the error rate for misclassifying APC beat was relative low at 6.32%, compared with previous research efforts.

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
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