模糊C-均值聚类算法在心率变异诊断中的应用研究  

Fuzzy C-means application studying on HRV diagnosis

作　　者：范丽玥[1,2] 高跃明[1,2] 陈旭海[3,2] 杜民[1,3,2] 陈建国[3,2] 

机构地区：[1]福州大学物理与信息工程学院,福建福州350108 [2]福州大学福建省医疗器械与医药技术重点实验室,福建福州350002 [3]福州大学电气工程与自动化学院,福建福州350108

出　　处：《福州大学学报（自然科学版）》2012年第3期334-340,共7页Journal of Fuzhou University(Natural Science Edition)

基　　金：科技部国际合作资助项目(2009DFA32050);福建省科技厅重点资助项目(2010I0017);福建省教育厅科研资助项目(JA09036)

摘　　要：对健康和心律异常的心电信号进行分析,经小波分解之后的心电信号实现R波的准确定位,提取心率变异性这一特征参数的5项指标.根据心率正常和异常体现在特征参数上的差异,用模糊C均值聚类算法得到心率变异的病症模式,并与正常心电数据进行比较.结果表明,心率变异特性具有明显的可分性,基于模糊C均值聚类算法可以得到较好的结果.Focused on ECG analysis of health and abnormal cases. Five indicators of the characteristic of heart rate variability are extracted after the signals that are decomposed by wavelet have been located via R point. Under the differences in the characteristic parameters of normal and abnormal, the dis- ease pattern of heart rate variability based on fuzzy C - means cluster are acquired, and a comparison is happened between disease and normal ECG data. It shows that heart rate variability is a clear sepa- rability parameter, meanwhile. Better results can be reached through the application of fuzzy C - means cluster algorithm.

关 键 词：R波 心率变异 模糊C均值聚类 

分 类 号：R318[医药卫生—生物医学工程]