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

Journal: Journal of healthcare engineering
Published Date: Jan 1, 2019

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

  • Tiantian Xie
    School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China.
  • Runchuan Li
    School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China.
  • Shengya Shen
    College of Information & Business, Zhongyuan University of Technology, Zhengzhou 450000, China.
  • Xingjin Zhang
    School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China.
  • Bing Zhou
    Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450001, China.
  • Zongmin Wang
    Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.

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