Discrimination of PVC Based on Multiple Cardiac Cycle fusion and Hermite Expansion Method

摘要：

Signal segmentation plays an important role in Electrocardiogram (ECG) feature extraction. In ECG signals, there are two kinds of dependencies: the dependencies in a single ECG cycle and the dependencies across ECG cycles. The proposed investigation focus on multiple cardiac cycle fusion for ECG feature extraction. Five different feature sets were generated using different ECG segmentation methods and redefinition methods of Premature ventricular contraction (PVC), which were not in medical significance. Hermite coefficients were used as ECG features. The proposed technique was employed to distinguish PVC from normal sinus rhythm (NSR). The data in the analysis were collected from MIT-BIH database. The experimental results show that the features extracted from multiple cardiac cycles classify better than that of single cardiac cycle.

关键词： feature extraction:ECG:sgnal segmentation mltiple cardiac cycles:dscrimination

作者: Ge Dingfei

作者单位: School of Information and Electronic Engineering Zhejiang University of Science and Technology Hang zhou,China

会议类型: 国际会议

会议名称: The 5th International Conference on Computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE10)

会议地点: 合肥

会议语种:英文

页码: 449-452

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）