ECG compression using wavelet transform and three-level quantization

Electrocardiogram signal is a very useful source of information for physicians in diagnosing heart abnormalities. With the increasing use of ECG in heart diagnosis, such as 24 hours monitoring or in ambulatory monitoring systems, the volume of ECG data that should be stored or transmitted, has greatly increased. This paper presents an efficient technique for compression of electrocardiogram (ECG) signals. In this technique, it is used the three level of quantization for thresholding. Then for encoding of samples we use an embedded of zero-tree wavelet (EZW) method and Huffman algorithms. The EZW algorithm allows an optimal data compression for a target bit rate and appeared superior to other wavelet-based ECG coders. Also, we have implemented different types of wavelet for compression of the ECG records from MIT-BIH database and compared their performances in the proposed algorithm. We have evaluated the effect of signal length and iterations of quantization on the compression ratio (CR). The correlation coefficient and percentage root mean square difference (PRD) is also measured. Simulation results show that the proposed method has good performance for especial types of wavelet transform. In general existing measures show how much reconstructed signal is similar to the original one.