ABSTRACT
An algorithm of the ECG signal compression, based on the combination of the run length encoding and discrete wavelet transform, intended for a simulated transmission via the IEEE 802.11b WLAN channel, is presented in this work. The algorithm consists of two basic phases that are ECG signal compression and transmission via the IEEE 802.11b WLAN channel. The algorithm is based on applying the run length coding upon the thresholded discrete wavelet transform of the real ECG signal. In terms of compression efficiency, applying the compression procedure on several ECG data, presenting diverse cardiac status, selected from the MIT-BIH arrhythmia data base, achieves compression ratio of around 10:1, normalized root mean squared error (NRMSE) of 4% and (mean± standard deviation) of the difference between the restituted ECG signal and the original one of around (3 10-6) ± 0.03. The end point of this work is to simulate transmission of the compressed ECG signal via the IEEE 802.11b WLAN channel. The unavoidable distortion introduced by the transmission channel reduces the compression ratio to about 6.7:1 in the cost of preserving the ECG signal fidelity.
- E. Kyriacou, S. Voskarides, C. S. Pattichis, R. Istepanian, M. S. Pattichis and C. N. Schizas 2002. Wireless Telemedicine Systems: A Brief Overview. IEEE Antennas & Propagation Magazine, vol. 44, 143--153.Google ScholarCross Ref
- JC Lin, 'Applying Telecommunication Technology to Healthcare Delivery' IEEE EMB Mag, vol 18, no 4, Jul/Aug 1999 pp. 28--31.Google Scholar
- I. Iacovides, C. S. Pattichis, C. N. Schizas. Editorial special issue on emerging health telematics applications in Europe. IEEE Trans. on Inform. Technol. in Biom., vol 2, no.3, 1998. Google ScholarDigital Library
- S. Tachakra, R. Istepanian. K. Banistas, "Mobile EHealth: the unwired evolution of telemedicine," Proceedings of HealthCom 2001, Italy, July 2001.Google Scholar
- C. S. Pattichis, E. Kyriacou, S. Voskarides, M. S. Pattichis, R. Istepanian, C. N. Schizas, "Wireless Telemedicine Systems: An Overview", IEEE Antennas & Propagation Magazine, vol. 44, 2002, pp. 143--153.Google Scholar
- U. Varshney, "Patient monitoring using infrastructureoriented wireless LAN", Int. J. Electronic Healthcare, vol. 2, 2006, pp. 149--163.Google Scholar
- N. Wickramasinghe, E. Geisler 'Encyclopedia of Healthcare Information Systems' Volume I A-D Medical Information science reference 2008, pp 157--166 Pedro de A. Berger, Francisco A. de O. Nascimento, Leonardo R. A. X. de Menezes, Adson F. da Rocha, Joao L. A. Carvalho 'Biomedical Signal Compression B'Google Scholar
- Gersho, A., & Gray, R. (1992). Vector quantization and signal compression. Kluwer Acad. Publication. Google ScholarDigital Library
- Paul S Addison 2005 Physiol. Meas. 26 (2005) R155--R199 Wavelet transforms and the ECG: a review IOP Publishing Ltd.Google Scholar
- Jalaleddine S, Hutchens C, Strattan R and Coberly W 1990 ECG data compression techniques---a unified approach IEEE Trans. Biomed. Eng. 37 pp 329--343.Google ScholarCross Ref
- Graps A. L. (1995) 'An introduction to wavelets' IEEE Computational Sciences and Engineering, Volume 2, Number 2, Summer 1995, pp 50--61. Google ScholarDigital Library
- Mallat S. (1999) A Wavelet Tour of Signal Processing, Academic Press, 2nd edition.Google Scholar
- Jawerth, B., Sweldens W. (1994) 'An Overview of Wavelet Based Multiresolution Analysis' SIAM Rev. 36, pp 377--412. Google ScholarDigital Library
- Paul Muhlethaler 802.11 et les réseaux sans fil, édition Eyrolles, 2002.Google Scholar
- Guy Pujolle les réseaux ÉDITIONS EYROLLES Edition 2008Google Scholar
- Istepanian R SHand PetrosianAA2000 Optimal zonal wavelet-based ecg data compression for mobile telecardiology system IEEE Trans. Inf. Technol. Biomed. 4 200--11. Google ScholarDigital Library
- Robert S. H. Istepanian, Leontios J. Hadjileontiadis, Stavros M. Panas "ECG Data Compression Using Wavelets and Higher Order Statistics Methods" IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 5, NO. 2, JUNE 2001 pp 108--115 Google ScholarDigital Library
- Michael Hilton 1997 wavelets and wavelets packet compression of electrocardiograms IEEE trans. biomed. engineering, 44, 394--402.Google Scholar
- X. Zhou 'ECG Compression Algorithms Comparisons among EZW, Modified EZW and Wavelet Based Linear Prediction' A thesis presented to the faculty of Binghamton University in partial fulfillments in mechanical engineering February 3, 2004Google Scholar
- Lu Z, Kim D Y and Pearlman W A 2000 Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm IEEE Trans. Biomed. Eng. 47 849--55.Google ScholarCross Ref
Index Terms
- Run length encoding and wavelet transform based ECG compression algorithm for transmission via IEEE802.11b WLAN channel
Recommendations
Research and improvement of ECG compression algorithm based on EZW
Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces the signal compression ratio.This paper studies the optimization of the EZW compression algorithm ...
Wavelet transform and bit-plane encoding
ICIP '95: Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1In this work a new approach for wavelet transform (WT) based image compression is presented. Employing a simple region representation coding scheme previously used with bi-level facsimile pictures, the wavelet transform coefficients are first quantized ...
Packet-size-Controlled ECG Compression Algorithm based on Discrete Wavelet Transform and Running Length Encoding
BIOSTEC 2015: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4This paper presents a development of new size-controlled compression algorithm for Electrocardiogram signal (ECG). Discrete Wavelet Transform (DWT) method, Bit-Field Preserving (BFP) and Running Length Encoding (RLE) are selected as compression tools in ...
Comments