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J Healthc Eng. 2019 Oct 7;2019:5787582. doi: 10.1155/2019/5787582. eCollection 2019.

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

Xie T1,2, Li R1,2, Shen S3, Zhang X1,2,4, Zhou B1,2, Wang Z1,2.

Author information

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.
State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China.


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.

Conflict of interest statement

The authors declare that there are no conflicts of interest.

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