کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
558179 874870 2013 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 8, Issue 1, January 2013, Pages 59–65
نویسندگان
, , ,