ECG compression using uniform scalar dead-zone quantization and conditional entropy coding.
A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. A discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are first quantized with a uniform scalar dead-zone quantizer, and then the quantized coefficients are decomposed into four symbol streams, representing a binary significance stream, the signs, the positions of the most significant bits, and the residual bits. An adaptive arithmetic coder with several different context models is employed for the entropy coding of these symbol streams. Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.
Chen
Jianhua
J
Department of Electronic Engineering, Yunnan University, Kunming 650091, PR China. chenjh@ynu.edu.cn
Wang
Fuyan
F
Zhang
Yufeng
Y
Shi
Xinling
X
D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods*; D019277:Entropy; D006801:Humans; D007089:Image Enhancement; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D012984:Software