基于数学形态学及支持向量机的心率失常识别

摘要

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%.%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％.