Paper Info
Title
ECG Signal Compression Based on Burrows-Wheeler Transformation and Inversion Ranks of Linear Prediction
Abstract
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.
Year
DOI
Venue
2007
10.1109/TBME.2006.888820
Biomedical Engineering, IEEE Transactions
Keywords
Field
DocType
electrocardiography,linear predictive coding,medical signal processing,Burrows-Wheeler transformation,ECG signal compression,electrocardiogram,inversion ranks,linear prediction,BW Transformation,inversions,lossless ECG compression,prediction
Data compression ratio,Burrows–Wheeler transform,Computer science,Discrete cosine transform,Linear prediction,Electronic engineering,Data compression,Lossless compression,Signal compression,Wavelet
Journal
Volume
Issue
ISSN
54
3
0018-9294
Citations 
PageRank 
References 
13
1.46
20
Authors
1
Name
Order
Citations
PageRank
Ziya Arnavut1131.46