ECG compression based on wavelet transform and context modeling arithmetic coding

A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. The discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance symbol stream, a sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bits stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experiments on several records from the MIT-BIH arrhythmia database showed that the proposed coding algorithm outperforms other well-known wavelet-based ECG compression algorithms.