Analysis of ECG Data Compression Techniques

V.S.R Kumari; Sridhar Abburi

doi:https://doi.org/10.14445/22315381/IJETT-V5N3P121

Research Article | Open Access | Download PDF

Volume 5 | Number 1 | Year 2013 | Article Id. IJETT-V5N3P121 | DOI : https://doi.org/10.14445/22315381/IJETT-V5N3P121

Analysis of ECG Data Compression Techniques

V.S.R Kumari , Sridhar Abburi

Citation :

V.S.R Kumari , Sridhar Abburi, "Analysis of ECG Data Compression Techniques," International Journal of Engineering Trends and Technology (IJETT), vol. 5, no. 1, pp. 116-124, 2013. Crossref, https://doi.org/10.14445/22315381/IJETT-V5N3P121

Abstract

ECG (electrocardiogram) is a test that measures the electrical activity of the heart. The heart is a muscular organ that beats in rhythm to pump the blood through the body. Large amount of signal data needs to be stored and transmitted. So, it is necessary to compress the ECG signal data in an efficient way. In the past decades, many ECG compression methods have been proposed and these methods can be roughly classified into three categories: direct methods, parameter extraction methods and transform methods. In this paper a comparative study of Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete sine Transform (DST) and Discrete Cosine Transform-II (DCT-II). Records selected from MIT-BIH arrhythmia database are tested. For performance evaluation Compression Ratio (CR), Percent Root Mean Square differences (PRD) are used.

Keywords

Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete sine Transform (DST), ECG data compression, ECG, Percentage Mean Square Difference (PRD), Compression Ratio (CR)

References

1. W. C. Mueller, “Arrhythmia detection program for an ambulatory ECG monitor,” Biomed. Sci. Instrum., vol. 14, pp. 81–85, 1978.
2. SangJoon Lee et.al, “A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device”, IEEE Transactions on Biomedical Engineering, VOL. 58, NO. 9, September 2011
3. J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, “AZTEC, apreprocessing program for real-time ECG rhythm analysis,” IEEE Trans. Biomed. Eng., vol. BME-15, no. 2, pp. 128–129, Apr. 1968.
4. B. Furht and A. Perez, “An adaptive real-time ECG compression algorithm with variable threshold,” IEEE Trans. Biomed. Eng., vol. 35, no. 6, pp. 489–494, Jun. 1988.
5. J. P. Abenstein, “Algorithms for real-time ambulatory ECG monitoring,” Biomed. Sci. Instrument., vol. 14, pp. 73–79, 1978.
6. R. C. Barr, S. M. Blanchard, and D. A. Dipersio, “SAPA-2 is the Fan,” IEEE Trans. Biomed. Eng., vol. BME-32, no. 5, p. 337, May 1985.
7. B. R. S. Reddy and I. S. N.Murthy, “ECG data compression using Fourier descriptors,” IEEE Trans. Biomed. Eng., vol.BME-33, no. 4, pp. 428–434, Apr. 1986.
8. M. E.Womble, J. S. Halliday, S.K. Mitter,M.C. Lancaster, and J. H. Triebwasser, “Data compression for storing and transmitting ECGs/VCGs,” Proc. IEEE, vol. 65, no. 5, pp. 702–706, May 1977.