摘要
提出用独立分量分析的方法对MIT-BIH心电数据库中的数据进行特征提取,再用改进的BP神经网络进行辨识.实验证明,此方法可以较准确地提取出PVC的特征参数,并能成功地判别正常的心电信号与PVC.
Premature Ventricular Contraction (PVC) is a common arrhythmia phenomenon and has important clinical significance, so it is important to detect and identify the PVC. This paper proposes using Independent Component Analysis to extract features from the data in the MIT-BIH ECG database, then identi- fies PVC with improved BP neural network. Experiment proves that this method can extract feature parameters of PVC more accurately, and succeed in discriminating normal ECG and PVC.
出处
《重庆工学院学报(自然科学版)》
2008年第5期80-84,共5页
Journal of Chongqing Institute of Technology
基金
重庆市自然科学基金资助项目(CSTC
2004B8)
关键词
室性早搏
独立分量分析
MIT-BIH
BP神经网络
premature ventricular contraction
independent component analysis
MIT-BIH
BP neural network