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Many 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.

The Block Sorting Lossless Data Compression Algorithm (BSLDCA) described by Burrows and Wheeler [3] has received considerable attention. It achieves as good compression rates as context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques [5]. This paper, describes the Lexical Permutation Sorting Algorithm (LPSA), its theoretical basis, and delineates its relationship to BSLDCA. In particular we describe how BSLDCA can be reduced to LPSA and show how LPSA could give better results than BSLDCA when transmitting permutations. We also introduce a new technique, Inversion hquencies, and show that it does as well as Move-to-hnt (MTF)Coding when there is locality of reference in the data.

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The Block Sorting Lossless Data Compression Algorithm (BWT) described by Burrows and Wheeler has received considerable attention. Its compression rates are simliar to context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques. This paper describes the Lexical Permutation Sorting Algorithm (LPSA), its theoretical basis and delineates its relationship to BWT. In particular we describe how BWT can be reduced to LPSA and show how LPSA could give better results than BWT when transmitting permutations.

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A recent development in data compression area is Burrows-Wheeler Compression algorithm (BWCA). Introduced by Burrows and Wheeler, the BWCA achieves compression ratio closer to the best compression techniques, such as partial pattern matching (PPM) techniques, but with a faster execution speed. In this paper, we analyze the combinatorial properties of the Burrows-Wheeler transformation (BWT), which is a block-sorting transformation and an essential part of the BWCA, introduce a new transformation, and delineate the new transformation with the BWT based on the multiset permutations.