摘要
为了评估亚健康状态,提出一种基于心电信号小波包变换和主成分分析的亚健康状态识别新方法。采用小波包变换对心电信号进行特征提取;再利用主成分分析(PCA)对所提特征进行降维处理,以剔除特征之间的冗余信息;最后应用线性判别式分析(LDA)对亚健康状态进行分类识别。研究结果显示,该方法能获得较高的识别率,对于实现亚健康状态的评估具有一定的参考价值。
In order to evaluate sub-health state,a new sub-health state recognition method based on wavelet packet transform and Principal Component Analysis（PCA） of ECG signals is discussed.The features of ECG signals are extracted by using wavelet packet transform,and the dimension of the features is reduced by utilizing PCA for removing the redundant information between them.Finally,Linear Discriminant Analysis（LDA） is applied on sub-health state recognition.The results demonstrate that this method can get higher recognition rate,which provides a certain reference value for achieving the assessment of sub-health state.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第26期238-241,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.30670529~~
关键词
亚健康状态
心电信号
小波包变换
主成分分析
线性判别式分析
sub-health state
ECG signals
wavelet packet transform
Principal Component Analysis（PCA）
Linear Discriminant Analysis（LDA）
