Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz., astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio ($\mathit{CR}\sim =20\text{ to }30$) and peak root mean square difference ($\mathit{PRD}\sim =0.01\text{ to }1.8$) both, hence the overall QS value is improved.

自从心电图的计算机辅助分析出现以来，心电图（ECG）信号的压缩就受到了研究人员的重视。在某些关键情况下，即。、宇航员、接受心脏监护的人、非卧床患者和动态心电图监测系统中，需要连续记录心电图数据并将其从一个位置传输到另一个位置。然而，记录数据的大小变得如此庞大，以至于它的数据传输实际上变得不可能。在本文中，提出了使用基于小波技术的 ECG 数据压缩，例如：a) 带游程编码 (RLE) 的小波包变换，b) 带 Huffman 编码的小波变换，c) 带 RLE 的小波变换和 d) 小波变换和 Lempel ZivWelch (LZW)。结果已使用 MIT-BIH（麻省理工学院/贝斯以色列医院）心律失常数据库进行了测试。以定量和定性方式检查这些方法的性能。从表格结果中，$\mathit{CR}～=20\text{ 至 }30$) 和峰值均方根差 ($\mathit{珠三角}～=0.01\text{ 至 }1.8$) 两者，因此整体 QS 值得到改善。