基于EMD的心电信号压缩算法

针对生物医学信号特别是心电信号(ECG)的特点和数据压缩需求，提出一种基于经验模态分解(EMD)方法的ECG信号压缩算法.所提算法计算简单，无需预先或后处理.以MIT-BIH标准数据库的心律失常数据作为实验数据，通过压缩比(CR)、均方根百分差异(PRD)、归一化均方根百分差异(PRDN)、均方根(RMS)、信噪比(SNR)、质量评分(QS)6个评价参数分析所提算法性能，并与基于小波分解的压缩算法进行比较.实验结果表明，所提算法具有较好的压缩比与保真度，证明了该算法的有效性.

ECG Compression Algorithm Based on Empirical Mode Decomposition

A compression algorithm based on empirical mode decomposition(EMD)was described in order to investigate the performance of EMD in biomedical signals， and especially in the case of electrocardiogram(ECG). The proposed algorithm was computationally simple to treat non stationary and nonlinear data without pre or post processing. In order to evaluate the performance of the proposed compression algorithm， MIT BIH arrhythmia database was applied， and some important parameters were obtained， such as the compress ratio(CR)， percent root mean square difference(PRD)， percent root mean square difference normalized(PRDN)， root mean square(RMS)， signal to noise ratio(SNR)， and quality score(QS)values. Experimental results indicated that the proposed algorithm has a better compress ratio and fidelity， which proved the feasibility of the proposed algorithm.