Premature ventricular contraction (PVC) is a common type of abnormal heartbeat. Without early diagnosis and proper treatment, PVC may result in serious harms. Diagnosis of PVC is of great importance in goal-directed treatment and preoperation prognosis. This paper proposes a novel diagnostic method for PVC based on Lyapunov exponents of electrocardiogram (ECG) beats. The methodology consists of preprocessing, feature extraction and classification integrated into the system. PVC beats can be classified and differentiated from other types of abnormal heartbeats by analyzing Lyapunov exponents and training a learning vector quantization (LVQ) neural network. Our algorithm can obtain a good diagnostic result with little features by using single lead ECG data. The sensitivity, positive predictability, and the overall accuracy of the automatic diagnosis of PVC is 90.26%, 92.31%, and 98.90%, respectively. The effectiveness of the new method is validated through extensive tests using data from MIT-BIH database. The experimental results show that the proposed method is efficient and robust. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
基金：
National Natural Science Foundation of China [61473112, 61203160]; Natural Science Foundation of Hebei Province [F2015201112]
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2016]版：
大类|3 区工程技术
小类|3 区计算机：跨学科应用3 区计算机：理论方法4 区工程：生物医学4 区医学：信息
最新[2025]版：
大类|2 区医学
小类|2 区计算机：跨学科应用2 区计算机：理论方法2 区工程：生物医学3 区医学：信息
JCR分区：
出版当年[2015]版：
Q1COMPUTER SCIENCE, THEORY & METHODSQ2COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ2MEDICAL INFORMATICSQ2ENGINEERING, BIOMEDICAL
最新[2023]版：
Q1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ1COMPUTER SCIENCE, THEORY & METHODSQ1ENGINEERING, BIOMEDICALQ1MEDICAL INFORMATICS
第一作者机构：[1]Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding, Peoples R China
通讯作者：
推荐引用方式(GB/T 7714)：
Liu Xiuling,Du Haiman,Wang Guanglei,et al.Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network[J].COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE.2015,122(1):47-55.doi:10.1016/j.cmpb.2015.06.010.
APA：
Liu, Xiuling,Du, Haiman,Wang, Guanglei,Zhou, Suiping&Zhang, Hong.(2015).Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,122,(1)
MLA：
Liu, Xiuling,et al."Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 122..1(2015):47-55
