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

Tiantian Xie; Runchuan Li; Shengya Shen; Xingjin Zhang; Bing Zhou; Zongmin Wang

doi:10.1155/2019/5787582

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

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

Authors

Tiantian Xie^{1

2}, Runchuan Li^{1

2}, Shengya Shen³, Xingjin Zhang^{1

2

4}, Bing Zhou^{1

2}, Zongmin Wang^{1

2}

Affiliations

¹ School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China.
² Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.
³ College of Information & Business, Zhongyuan University of Technology, Zhengzhou 450000, China.
⁴ State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Databases, Factual
Decision Trees
Electrocardiography*
Humans
Machine Learning*
Signal Processing, Computer-Assisted*
Ventricular Premature Complexes / diagnosis*