ECG Characteristic Points Detection using General Regression Neural Network-Based Particle Filters

摘要：

Characteristic points (CPs) detection is still an open problem for the automatic analysis of electrocardiogram (ECG). Past Kalman Filter-Based efforts to extract CPs rely on a locally linearized approximation of the nonlinear ECG dynamical model and fail to detect all CPs accurately for strong noisy ECG. In this study, an improved particle filters-based algorithm is developed to track the dynamical ECG morphology and localize its characteristic points in strong noisy environments. Experiments on real ECG records contaminated by different coloration noise clearly show the superior performance of the presented approach over the Kalman Filter method.

作者: Guo-Jun Li Xiao-na Zhou Shu-ting Zhang Nai-Qian Liu

作者单位: College of Communication Engineering, Chongqing University, Shapingba Chongqing, 400044, China. He i Chongqing Communication Institute, Shapingba Chongqing, 400035, China College of Communication Engineering, Chongqing University, Shapingba Chongqing, CO 400044 China

会议类型: 国际会议

会议名称: 2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)

会议地点: 苏州

会议语种:英文

页码: 155-158

在线出版日期: 2011-11-03（万方平台首次上网日期，不代表论文的发表时间）