摘要
为实现对不同类型的心电图自动分析,研究并提出了一种顺序筛选极大值的R波定位算法,并采用支持向量机(SVM)进行最后的心律失常心拍识别。定位算法以数学形态学为基础,结合心电图自身特点,定义R波筛选区间,避免了传统算法中的阈值选择;定位R波峰后以R波峰为中心提取不同类型的心率失常的心拍,选择径向基(RBF)支持向量机进行识别分类。使用MIT-BIH心率失常数据库文件进行实验仿真,结果表明,算法对含不同类型心拍的心电图R波峰正确检测率较高(99.36%),学习后的SVM能有效识别早搏、房颤、束支传导阻滞、正常等不用类型心拍,总体识别率达到99.75%。
To achieve automatic analysis for different types of ElectroCardioGraph（ECG）,a sequential screening method for maximum value was brought to detect R wave,while Support Vector Machine（SVM） was used to identify arrhythmia heart beats finally.The localization algorithm based on mathematical morphology combined with characteristics of ECG defined R-wave screening interval to avoid threshold selection in traditional algorithm.After R-peaks being positioned,various types of arrhythmia heart beats were extracted with R wave crest as its center and classified by selecting Radial Basis Function（RBF） or SVM.The results of the simulation experiment on the MIT-BIH database files indicate that this algorithm acquired high relevance ratio at 99.36% for ECG with different types of heart beats.After learning,the SVM can effectively identify as many as 4 types,such as atrial premature beat,premature ventricular beat,bundle branch block and normal heart beat,the overall recognition rate is 99.75%.
出处
《计算机应用》
CSCD
北大核心
2013年第4期1173-1175,共3页
journal of Computer Applications
关键词
心电图
数学形态学
R波检测
心律失常分类
支持向量机
ElectroCardioGram（ECG）
mathematical morphology
R wave detection
arrhythmia classification
Support Vector Machine（SVM）
