A Convolutional Neural Network for Identifying Premature Ventricular Contraction Beat and Right Bundle Branch Block Beat

2018 ◽  
Author(s):  
Yuwei Zhang ◽  
Jianxun Yu ◽  
Yuan Zhang ◽  
Chengyu Liu ◽  
Jianqing Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Right Bundle Branch Block ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Bundle Branch Block

A Modified Convolutional Neural Network for ECG Beat Classification

2020 ◽  
Vol 10 (3) ◽  
pp. 654-660
Author(s):  
Lulu Yang ◽  
Junjiang Zhu ◽  
Tianhong Yan ◽  
Zhaoyang Wang ◽  
Shangshi Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Premature Ventricular Contraction ◽  
Average Score ◽  
Bundle Branch Block ◽  
Rr Intervals ◽  
Normal Sinus ◽  
Beat Classification ◽  
The Difference ◽  
Using Data

Most convolutional neural networks (CNNs) used to classify electrocardiogram (ECG) beats tend to focus only on the beat, ignoring its relationships with its surrounding beats. This study aimed to propose a hybrid convolutional neural network (HCNN) structure, which classified ECG beats based on the beat's morphology and relationship such as RR intervals. The difference between the HCNN and the traditional CNN lies in the fact that the relationship can be added to any layer in the former. The HCNN was fed with RR intervals at 3 different positions, trained using data from 2170 patients. It was then evaluated with labeled clinical data from 2102 patients to classify ECG beats into premature ventricular contraction beat, atrial premature contraction beat (APC), left bundle branch block beat, right bundle branch block beat, and normal sinus beat. The results showed that the performance of the proposed HCNN method (with an average score of 86.61% on 12 leads) was 3.31% higher than that of the traditional CNN (83.30%) on the test set. In particular, the APC improved most significantly from 57.67% to 76.92% in terms of sensitivity and from 58.80% to 78.46% in terms of the positive predictive value in lead V1.

Very Large-Scale Integration for Premature Ventricular Contraction Detection Using a Convolutional Neural Network

2021 ◽  
Author(s):  
Yuan-Ho Chen ◽  
Hsin-Tung Hua
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Premature Ventricular Contraction ◽  
Very Large Scale Integration ◽  
Cmos Process ◽  
Clock Frequency ◽  
Ventricular Contraction ◽  
Large Scale Integration ◽  
Scale Integration

We propose a very large-scale integration (VLSI) chip for premature ventricular contraction (PVC) detection. The chip contains a convolutional neural network (CNN) for detecting the abnormal heartbeats associated with PVCs in 12-lead electrocardiogram signals. The proposed CNN comprises two convolutional layers and a fully connected layer; in testing, it achieved a high PVC detection accuracy of [Formula: see text]. Created by using a [Formula: see text]-[Formula: see text]m CMOS process, the developed chip consumes [Formula: see text] mW with a clock frequency of 50 MHz and gate count of [Formula: see text] K. Compared with the previously designed VLSI chips, the proposed CNN chip achieves higher accuracy in abnormal heartbeat detection.

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 Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1790
Author(s):  
Junsheng Yu ◽  
Xiangqing Wang ◽  
Xiaodong Chen ◽  
Jinglin Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Time Window ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Ventricular Contraction ◽  
Test Set ◽  
One Dimensional

Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing.

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