A Zerotree Coding for Compression of ECG Signal Using EZW and SPIHT

Biomedical waveforms, such as electrocardiogram (ECG), always posses a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data to compress the ECG is vital. This paper evaluates the compression performance and characteristics of zerotree coding compression schemes of ECG applications. Two methods, namely the Embedded Zerotree Wavelet (EZW) and the Set Partitioning In Hierarchical Tree (SPIHT) are proposed. The EZW is one of the first algorithms to show the full power of wavelet based image compression. The SPIHT algorithm is a highly refined version of the EZW algorithm. EZW and SPIHT have achieved notable success in still image coding. We modified these algorithms for applied it to compression of ECG data. Both methodologies were evaluated using the percent root mean square difference (PRD) and the Compression Ratio (CR). Theoretical results are contrasted with a simulation study with actual ECG signals from MIT-BIH arrhythmia database. The simulation results show that the both methods achieve a very significant improvement in the performances of compression ratio and error measurement for ECG, as compared with some other compression methods.