A wavelet based progressive ECG compression using modified SPIHT
An Electrocardiogram (ECG) signal contains much vital clinical information. These signals include information on cardiac imperfection. These signals recorded over an extended period result in large file size. Thus, compression is essential. This paper aims to implement compression of ECG signal using wavelet based progressive coding such as set partitioning in hierarchical trees. Further improvement in the compression ratio is possible with modified run length encoding. The experimentation performed with the MIT BIH arrhythmia database shows compression ratio of 16:1 and average mean square difference percentage of 0.58 using bior4.4 wavelet. The coders achieve bit rate control and produce a bitstream that is progressive in quality. The user can trim the bitstream at any point and make the best quality restoration for reduced file size using required values of compression ratio.