一种基于心电数据压缩的小波元选择方法  

Choosing Method of Wavelet Neuron Based on ECG Data Compression

作　　者：郭巧惠[1] 杨永明[1] 古良玲[1] 

机构地区：[1]重庆大学电气工程学院高电压与电工新技术教育部重点实验室,重庆400030

出　　处：《重庆大学学报（自然科学版）》2006年第10期20-23,37,共5页Journal of Chongqing University

基　　金：教育部重点实验室资助项目(718411003)

摘　　要：介绍了用于心电数据压缩而构造的一种小波神经网络以及它的小波元选择的方法.提出了根据对心电数据做频谱估计,确定其时频域支撑,再根据小波的时频特性确定小波函数的时频域支撑,来初步选定小波元,然后再利用OLS算法对初选的小波元进行筛选.选用Morlet小波为母小波,并用一段心电信号来对该方法进行验证.验证结果表明落在心电数据频谱范围内的Morlet小波有152个,再通过OLS算法筛选后小波元数大大减少了,使得小波网络的尺寸趋于最优.在网络训练时,训练时间也明显地减少.The paper discusses a wavelet network for the ECG data compression and proposes the method for choosing its wavelet neuron. According to the spectrum range of the ECG data, we decide the time-frequency field of ECG. And the time-frequency field of wavelet is also determined by the spectrum range of it. The wavelet neuron is fixed preliminarily by the first two steps. Then the preliminary wavelet neuron is screened by using OLS algorithm. We choose Morlet as the mother wavelet, and use the ECG signal to validate by the method. The result demonstrates that the number of Morlet whose spectrums locate at the ECG＇s is up to 152. But after screening by the OLS algorithm, it reduces sharply. This method can make the size of the wavelet network driving to optimum and also reduce the training time of the wavelet network sharply.

关 键 词：小波网络 小波元 OLS算法 

分 类 号：TM935.2[电气工程—电力电子与电力传动]