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Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest.
J Healthc Eng 2019; 2019:5787582JH

Abstract

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.

Authors+Show Affiliations

School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.College of Information & Business, Zhongyuan University of Technology, Zhengzhou 450000, China.School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China.School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31687121

Citation

Xie, Tiantian, et al. "Intelligent Analysis of Premature Ventricular Contraction Based On Features and Random Forest." Journal of Healthcare Engineering, vol. 2019, 2019, p. 5787582.
Xie T, Li R, Shen S, et al. Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. J Healthc Eng. 2019;2019:5787582.
Xie, T., Li, R., Shen, S., Zhang, X., Zhou, B., & Wang, Z. (2019). Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. Journal of Healthcare Engineering, 2019, p. 5787582. doi:10.1155/2019/5787582.
Xie T, et al. Intelligent Analysis of Premature Ventricular Contraction Based On Features and Random Forest. J Healthc Eng. 2019;2019:5787582. PubMed PMID: 31687121.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. AU - Xie,Tiantian, AU - Li,Runchuan, AU - Shen,Shengya, AU - Zhang,Xingjin, AU - Zhou,Bing, AU - Wang,Zongmin, Y1 - 2019/10/07/ PY - 2019/05/29/received PY - 2019/08/22/revised PY - 2019/09/09/accepted PY - 2019/11/6/entrez PY - 2019/11/7/pubmed PY - 2019/11/7/medline SP - 5787582 EP - 5787582 JF - Journal of healthcare engineering JO - J Healthc Eng VL - 2019 N2 - 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. SN - 2040-2309 UR - https://www.unboundmedicine.com/medline/citation/31687121/Intelligent_Analysis_of_Premature_Ventricular_Contraction_Based_on_Features_and_Random_Forest L2 - https://dx.doi.org/10.1155/2019/5787582 DB - PRIME DP - Unbound Medicine ER -