• IEEE Trans Biomed Eng · Mar 2007

    ECG signal compression based on Burrows-Wheeler transformation and inversion ranks of linear prediction.

    • Ziya Arnavut.
    • Department of Computer Science, SUNY Fredonia, Fredonia, NY 14063, USA. arnavut@fredonia.edu
    • IEEE Trans Biomed Eng. 2007 Mar 1; 54 (3): 410-8.

    AbstractMany transform-based compression techniques, such as Fourier, Walsh, Karhunen-Loeve (KL), wavelet, and discrete cosine transform (DCT), have been investigated and devised for electrocardiogram (ECG) signal compression. However, the recently introduced Burrows-Wheeler Transformation has not been completely investigated. In this paper, we investigate the lossless compression of ECG signals. We show that when compressing ECG signals, utilization of linear prediction, Burrows-Wheeler Transformation, and inversion ranks yield better compression gain in terms of weighted average bit per sample than recently proposed ECG-specific coders. Not only does our proposed technique yield better compression than ECG-specific compressors, it also has a major advantage: with a small modification, the proposed technique may be used as a universal coder.

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