With Cutoff Tcq-based Ecg Signal Compression

The research on heart disease is an important topic in clinical medicine. The electrocardiogram (ECG) has been an important tool for the diagnosis of heart disease. With the development of modern medicine, higher level of requirements regarding the storage, processing and transmission of large amount of ECG data are presented. Therefore, the efficient ECG data compression is of practical importance.This paper presents an effective method of ECG signal compression. The method is divided into three parts, namely: wavelet transform, quantization and coding.For wavelet transform, the theory of wavelet transform is described first, and then the 5 level discrete wavelet transform (DWT) based on the 9/7 filter bank which is suitable for ECG data compression is selected.For quantization, a dead-zone trellis coded quantizer (DZTCQ) is presented. It is applied to quantize the wavelet transform coefficients of ECG signals. Trellis coded quantizer(TCQ)is a quantization method which make use of convolutional encoding, signal space expanding to increase Euclidian distance and codebook partition. Based on the characteristics of the wavelet coefficients of ECG signals, DZTCQ presents higher quantization gain over the traditional TCQ. We also determine an important parameter by experiments, which can improve the quantization results.For entropy coding, since the symbol set of the quantized coefficients are too large to be compressed efficiently, the quantized coefficients are broken down into four symbol streams with smaller alphabets. Then we build different Context models respectively for the four symbol streams. Finally, the quantization coefficients are coded by adaptive arithmetic coding combined with the Context modelsThe ECG data records used in experiment are from MIT-BIH arrhythmia database. Simulation results show that the presented scheme surpasses some recently developed ECG compression scheme in the sense of lower distortion at the same compression ratios.