Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, th...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Tiantian, Li, Runchuan, Shen, Shengya, Zhang, Xingjin, Zhou, Bing, Wang, Zongmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800940/
https://www.ncbi.nlm.nih.gov/pubmed/31687121
http://dx.doi.org/10.1155/2019/5787582
_version_ 1783460501876899840
author Xie, Tiantian
Li, Runchuan
Shen, Shengya
Zhang, Xingjin
Zhou, Bing
Wang, Zongmin
author_facet Xie, Tiantian
Li, Runchuan
Shen, Shengya
Zhang, Xingjin
Zhou, Bing
Wang, Zongmin
author_sort Xie, Tiantian
collection PubMed
description Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, this paper proposes a method based on the combination of features and random forest to identify PVC. The RR intervals (pre_RR and post_RR), R amplitude, and QRS area are chosen as the features because they are able to identify PVC better. The experiment was validated on the MIT-BIH arrhythmia database and achieved good results. Compared with other methods, the accuracy of this method has been significantly improved.
format Online
Article
Text
id pubmed-6800940
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-68009402019-11-04 Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest Xie, Tiantian Li, Runchuan Shen, Shengya Zhang, Xingjin Zhou, Bing Wang, Zongmin J Healthc Eng Research Article Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, this paper proposes a method based on the combination of features and random forest to identify PVC. The RR intervals (pre_RR and post_RR), R amplitude, and QRS area are chosen as the features because they are able to identify PVC better. The experiment was validated on the MIT-BIH arrhythmia database and achieved good results. Compared with other methods, the accuracy of this method has been significantly improved. Hindawi 2019-10-07 /pmc/articles/PMC6800940/ /pubmed/31687121 http://dx.doi.org/10.1155/2019/5787582 Text en Copyright © 2019 Tiantian Xie et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xie, Tiantian
Li, Runchuan
Shen, Shengya
Zhang, Xingjin
Zhou, Bing
Wang, Zongmin
Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title_full Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title_fullStr Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title_full_unstemmed Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title_short Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
title_sort intelligent analysis of premature ventricular contraction based on features and random forest
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800940/
https://www.ncbi.nlm.nih.gov/pubmed/31687121
http://dx.doi.org/10.1155/2019/5787582
work_keys_str_mv AT xietiantian intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest
AT lirunchuan intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest
AT shenshengya intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest
AT zhangxingjin intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest
AT zhoubing intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest
AT wangzongmin intelligentanalysisofprematureventricularcontractionbasedonfeaturesandrandomforest

Ejemplares similares