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

被引：12
|
作者：
Xie, Tiantian [1 ,2 ]
Li, Runchuan [1 ,2 ]
Shen, Shengya [3 ]
Zhang, Xingjin [1 ,2 ,4 ]
Zhou, Bing [1 ,2 ]
Wang, Zongmin [1 ,2 ]
机构：
[1] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Henan, Peoples R China
[2] Zhengzhou Univ, Cooperat Innovat Ctr Internet Healthcare, Zhengzhou 450000, Henan, Peoples R China
[3] Zhongyuan Univ Technol, Coll Informat & Business, Zhengzhou 450000, Henan, Peoples R China
[4] State Key Lab Math Engn & Adv Comp, Zhengzhou 450000, Henan, Peoples R China
来源：
JOURNAL OF HEALTHCARE ENGINEERING | 2019年 / 2019卷
基金：
中国国家自然科学基金;
关键词：
FUZZY NEURAL-NETWORK; WAVELET TRANSFORM; CLASSIFICATION; SELECTION;
D O I：
10.1155/2019/5787582
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
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.
引用
收藏
页数：10
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