1. Electro-CardioGram (ECG) Data Compression Bhavya R. Vijay V. Asst. Prof, Dept of T.C.E, Asst. Prof, Dept of T.C.E K.S.I.T., Bangalore-62 K.S.I.T, Bangalore-62 for National Conference on Communications & Computations At K. S. Institute of Technology On 29 th Sep 2012 1

2. To have a lossless or a near lossless ECG Data Compression To have an appreciable Compression Ratio (CR) that can reduce memory required to store ECG data Objectives of the proposed work 2

3. Contd… To minimize Percentage-Root-mean- Square Difference (PRD) to an extremely low value To propose a suitable decoding and dequantization to reconstruct the original ECG signal 3

4. 4 Why ECG data Compression ? Electro-Cardiogram data is pretty much required from the clinical point of use. Many a times doctors would fix the ECG monitoring device in patients body for a reasonably long duration ( may be a week) to check at which time heart would function bit differently Hence data collected for so many days would be really long hence needed to be compressed, but keeping the fact that critical information should not be lost..

5. Inferences from Literature Survey ecg paper.xlsx ecg paper.xlsx 5 Scalar quantization leads to lossy compression. Hence PRD is very high Wavelet transforms provide low PRD value but Compression ratio is very less ( <10:1). Hence requires more storage space Compression based on neural network provides a bad reconstruction of original signal Sparse decomposition and compression technique provide a normal CR around 6.5:1, but PRD value is quite high ( >7%)

6. Methodology Input ECG data Quantization of Input data Encoding Quantized data Decoding encoded data Dequantization Reconstruction of ECG data 6

7. Possible Outcome To Propose a suitable algorithm that does ECG data compression with a comparatively high CR value and extremely low PRD value to provide very high quality of reconstructed ECG signal for clinical use. ECG data so compressed can be transmitted to clinical analysis easily as the size of the data is less. This makes the possibility of extending Health services to remote places. To present paper on the same for national and international conferences and journals for publication 7

8. References 8 S.K.Mukhopadhyay, M.Mitra “An ECG Data Compression Method via R-Peak Detection and ASCII Character Encoding” ICCCET 2011, 18th & 19th March, 2011. Iman Mohammad Rezazadeh, Sanaz Parvaresh, M. Erfan H. E. Zargar, Joshua Proulx “ECG Data Compression for Mobile Phone Tele-Cardiology Applications Using.NET Framework ” 978-1-4244-7000-6/11/$26.00 ©2011 IEEE Bo Yu, Liuqing Yang, Chia-Chin Chong “ECG Monitoring over luetooth: Data Compression and Transmission” 978-1-4244- 6398-5/10/$26.00 ©2010 IEEE. L. W. Gardenhire, “Redundancy reduction the key to adaptive Telemetry,” in Proc. 1964 Nat. Telemetry Con$, 1964, pp. 1-16. U.E. Ruttimann, H.V. Pipberger, “Compression of the ECG by prediction or interpolation and entropy coding,” IEEE Trans. Biomed. Eng. Vol. 26, pp. 613-623, Nov. 1979. C.T. Ku, H. S. Wang, K.C. Hung, Y.S. Hung, “A Novel ECG Data Compression Method Based on Nonrecursive Discrete Periodized Wavelet Transform”, IEEE Trans. Biomed. Eng., vol. 53(12), pp. 2577–2583, Dec. 2006. R. Benzid, A. Messaoudi, and A. Boussaad, “Constrained ECG compression algorithm using the block-based discrete cosine transform,” Digital Signal Processing, vol. 18, no. 1, pp. 56–64, January 2008. Z. Lu, D. Y. Kim, and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm,” IEEE Trans. on Biomedical Engineering, vol. 47, no. 7, pp. 849–856, July 2000. A. Al-Shrouf, M. Abo-Zahhad, and S. M. Ahmed, “A novel compression algorithm for electrocardiogram signal based on the linear prediction of the wavelet coefficients,” Digital Signal Processing, vol. 13, pp. 604–622, October 2003. I. M. Rezazadeh. M. H.Moradi, AM. Nasrabadi, " Implementing of SPIRT and subband Energy Compression (SEC) Method on Two Dimensional ECG Compression: A Novel Approach. Engineering in medicine and biology, 27"'annual conference, IEEE, China, 2005.