ECG signal compression based on Burrows-Wheeler transformation and inversion ranks of linear prediction.
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.
Arnavut
Ziya
Z
Department of Computer Science, SUNY Fredonia, Fredonia, NY 14063, USA. arnavut@fredonia.edu
D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted