北京生物医学工程

一种基于小波域主成分分析的心电压缩算法

An ECG compression algorithm based on wavelet domain principal component analysis

作者：张彪邱天爽

单位：大连理工大学电子信息与电气工程学部(辽宁大连116024)

分类号：R318.04

出版年·卷·期（页码）：2016·35·3（254-258）

摘要：

目的为降低心电信号存储和传输的数据量，并克服传统心电压缩方法只利用导联内相关性的劣势,本文提出一种基于小波域主成分分析和分层编码（wPCA_LC）的压缩方法。方法首先通过心电电极获取12通道心电数据，对所有通道的心电信号做小波变换，每个尺度下的小波系数组成小波系数矩阵，在每个系数矩阵上做主成分分析（principal component analysis,PCA），之后对小波系数小的主成分做［位置增量，数据］的编码方式，其他主成分采用霍夫曼编码，最后使用本文算法压缩圣彼得堡心率失常数据库。结果实验表明，在均方根误差为5.2%时，本文算法的压缩比为71，远高于基于稀疏分解的方法和基于小波变换阈值选择的方法。结论基于小波域主成分分析的心电压缩算法对多导联心电信号具有较好的压缩性能。

Objective In order to reduce the amount of ECG signal to be stored and transferred,and to overcome the disadvantage of only making use of correlation in leads in traditional ECG compression methods,this paper proposes a compression method based on wavelet domain principal component analysis and hierarchical coding (wPCA_LC).Methods First we obtain ECG data of 12 channels through the ECG electrodes,and transform ECG signals of all channels into wavelet domain,each scale of the wavelet coefficient forms the wavelet coefficient matrixs.Then we run principal component analysis (PCA) on each coefficient matrix,encoding the wavelet coefficient with small amplitude with ［position increment,data］ style,other components adopt Huffman coding.At last,Physionet St.Petersburg INCART 12-lead Arrhythmia Database is tested by this method.Results Experiments show that when the root mean square error reaches 5.2%,this method’s compression ratio can achieve 71,much higher than the method based on sparse decomposition and threshold selection method based on wavelet transform.Conclusions wPCA_LC method has good compression performance on multi-channel ECG signal.

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