DOI:
https://doi.org/10.14483/22484728.262Published:
2011-06-30Issue:
Vol. 4 No. 1 (2010)Section:
A Research VisionAnálisis multi-resolución y codificación sin pérdida de información en la compresión de señales electrocardiográficas
Multi-resolution analysis and lossless encoders in the compression of electrocardiographic signals
Keywords:
DWT, encoders, compression rate, percentage root mean square difference. (es).Downloads
Abstract (es)
Many algorithms have been developed in the compression on biomedical signals. One of the most commonly compressed signals correspond to the electrocardiographic signal because is frequently measurement in environment hospitals. In this paper, we present the comparison of two methods based on multi-resolution analysis and lossless encoders, specifically Discrete Wavelet Transform and Huffman-Run length. In the
performance of the algorithms, we analyzed two parameters: the compression ratio (CR) and the percentage of roots mean square (PRD). Our algorithms presented a CR of 8:1 to a PRD of 0,5%, which are good results in clinical applications, because the main characteristics in time and frequency are preserved.
References
D. Ballesteros et al., "Compresión de señales ECG utilizando umbralización wavelet y codificación Huffman", Quinto Congreso deElectrónica, Control y Telecomunicaciones, pp. 19-30, Jun. 2009.
D. Ballesteros et al., "Compresión de señales ECG utilizando DWT y codificación Huffman", Revista Scientia et Technica. Año XV, no. 41, pp. 340-345, Abr. 2009.
M. Cristiano, R. Rosanna and B. Ivanil. "Lossless compression applied to sequence of bits", 2007. Available on (5-4-2009): http://ww.dt.fee.unicamp.br/~ivanil/lossless_bitmap_agulhari_2007.pdf
S. Pathomvanh and S. Airphaiboon "ECG Data Compression using Adaptive Beat Subtraction Method". International Symposium on Communications and Information Technologies, pp. 477-481, Oct. 2008.
S. Mallat . "A wavelet tour of signal processing". London: Elsevier Inc. 1999, p. 4.
M. Brito et al. "A predictive adaptive approach to generic ECG data compression". IEEE International Workshop on Intelligent Signal Processing, pp-32-37, Sep. 2005.
1.png)
.jpg)
