ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden for the long-term recording system and telemedicine applications. In this paper, an improved wavelet-based compression method is proposed. A discrete wavelet transform (DWT) is firstly applied to the mean removed ECG signal. DWT coefficients in a hierarchical tree order are taken as the component of a vector named tree vector (TV). Then, the TV is quantized with a vector–scalar quantizer (VSQ), which is composed of a dynamic learning vector quantizer and a uniform scalar dead-zone quantizer. The context modeling arithmetic coding is finally employed to encode those quantized coefficients from the VSQ. All tested records are selected from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. Statistical results show that the compression performance of the proposed method outperforms several published compression algorithms.

► An improved wavelet-based compression method is proposed for the ECG signal.
► The vector–scalar quantization updates the codebook to match the unknown data source.
► The context modeling arithmetic coding squeezes the redundancy extremely.
► Experimental result of the threshold relationship meets the theoretical derivation.
► Compression performance of our method outperforms recently published algorithms.