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International Journal of High Performance Computing Applications
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Lossless and Near-Lossless Compression of Ecg Signals with Block-Sorting Techniques

Ziya Arnavut

Department of Computer Science, Suny Fredonia, Fredonia, NY 14063, ziya.arnavut{at}fredonia.edu

In this work, we investigate the lossless and near-lossless compression of electrocardiogram (ECG) signals with different block-sorting transformations. We show that transformations with smaller context depths are a better choice for ECG signal compression when speed and memory utilization are considered. Further, we show that compression results of our proposed technique is better than other well known techniques, such as bzip2, gzip, and the shorten waveform coder.

Key Words: prediction • Burrows Wheeler Transformation • ECG compression

International Journal of High Performance Computing Applications, Vol. 21, No. 1, 50-58 (2007)
DOI: 10.1177/1094342006074860
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