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ECG Signal Compression Based on Burrows-Wheeler Transformation and Inversion Ranks of Linear Prediction

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1 Author(s)
Ziya Arnavut ; Dept. of Comput. Sci., SUNY Fredonia, NY

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

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:54 ,  Issue: 3 )