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ECG compression using the context modeling arithmetic coding with dynamic learning vector-scalar quantization
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DOI：10.1016/j.bspc.2012.04.003.png)
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Abstract
En 中文
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. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
ECG compression
Vector quantization
Scalar quantization
Context model
Conditional entropy coding
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