Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest
Joint Authors
Xie, Tiantian
Li, Runchuan
Shen, Shengya
Zhang, Xingjin
Zhou, Bing
Wang, Zongmin
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xie, Tiantian& Li, Runchuan& Shen, Shengya& Zhang, Xingjin& Zhou, Bing& Wang, Zongmin. 2019. Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175267
Modern Language Association (MLA)
Xie, Tiantian…[et al.]. Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. Journal of Healthcare Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1175267
American Medical Association (AMA)
Xie, Tiantian& Li, Runchuan& Shen, Shengya& Zhang, Xingjin& Zhou, Bing& Wang, Zongmin. Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175267
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175267