Detecting PVC Beats by Beat-by-beat Analysis of ECG Signals Using Machine Learning Classifiers for Real-time Predictive Cardiac Health Monitoring-Reference-Cited by-同舟云学术

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Detecting PVC Beats by Beat-by-beat Analysis of ECG Signals Using Machine Learning Classifiers for Real-time Predictive Cardiac Health Monitoring

Published:2022-10-26 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
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Author:

Tsai I Hua¹,Morshed Bashir I.¹

Affiliation:

1. Texas Tech University,Department of Computer Science,Lubbock,TX,USA,79409

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9965569/9965624/09965650.pdf?arnumber=9965650

Reference20 articles.

1. Artificial intelligence classification methods of atrial fibrillation with implementation technology

2. Detecting atrial fibrillation by deep convolutional neural networks.Comput;xia;Biol Med,2018

3. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

4. Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System;ashif;2018 World Journal of Engineering and Technology,0

5. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Beat-wise Classification of Arrhythmia Using Novel Combination of ECG Signal-Specific and Signal-Independent Features with ML Algorithms;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

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