Have a personal or library account? Click to login
Performance Analysis of ECG Signal Compression using SPIHT Cover

References

  1. S. Chaudhuri, D. .Tanmay, and S. Duttagupta, AEE Embulation Analysis in Wearable ECG. New York, New York, USA: Springer, 2009.10.1007/978-1-4419-0724-0
  2. X. Zheyuan, F. Xiaping, L. Shaoqiang, L. Yongzhou, and Z. Huan, “Performance Analysis forDct-Based Coded Image Communication in Wireless Multimedia Sensor,” International Journal On Smart Sensing and Intelligent Systems, vol. 6, no. 1, pp. 120–135, 2013.10.21307/ijssis-2017-531
  3. C. Huang and S. Miaou, “Transmitting SPIHT compressed ECG data over a nextgeneration mobile telecardiologytestbed,” 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 3525–3528, 2001.
  4. Z. Lu, D. Y. Kim, and W. a Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm.,” IEEE transactions on bio-medical engineering, vol. 47, no. 7, pp. 849–56, Jul. 2000.10.1109/10.84667810916255
  5. M. Pooyan, A. Taheri, M. Moazami-goudarzi, I. Saboori, and A. Introduction, “Wavelet Compression of ECG Signals Using SPIHT Algorithm,” in World Academy of Science, Engineering and Technology 2, 2005, vol. 2, no. 3, pp. 212–215.
  6. M. Moazami-goudarzi, M. H. Moradi, and S. Abbasabadi, “Method for Electrocardiogram Compression Using Two Dimensional Multiwavelet Transform,” Computer, no. 1, pp. 1–5.
  7. I. Mohammad Rezazadeh, M. Hassan Moradi, and A. MotieNasrabadi, “Implementing of SPIHT and Sub-band Energy Compression (SEC) Method on Two-Dimensional ECG Compression: A Novel Approach.,” Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society. Conference, vol. 4, pp. 3763–6, Jan. 2005.
  8. S.-C. Tai, C.-C.Sun, and W.-C. Yan, “A 2-D ECG compression method based on wavelet transform and modified SPIHT.,” IEEE transactions on bio-medical engineering, vol. 52, no. 6, pp. 999–1008, Jun. 2005.10.1109/TBME.2005.84672715977730
  9. E. Sharifahmadian, “Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.,” Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society. Conference, vol. 1, pp. 5238–43, Jan. 2006.
  10. S. M. E. Sahraeian and E. Fatemizadeh, “Wavelet-Based 2-D ECG Data Compression Method Using SPIHT and VQ Coding,” in Proceeding EUROCON 2007 The International Conference on “Computer as a Tool”, 2007, pp. 133–137.10.1109/EURCON.2007.4400442
  11. S. Nayebi, M. H. Miranbeigi, and A. M. Nasrabadi, “Wavelet Based 2-D ECG Compression by Implementing of SPIHT Algorithm and RL Coding,” Construction, no. 2, pp. 1349–1353, 2008.
  12. T. Lu, K. Wen, and P. Chang, “Block Reordering Wavelet Packet SPIHT Image Coding,” Image (Rochester, N.Y.), pp. 442–449, 2001.10.1007/3-540-45453-5_57
  13. A. Sargolzaei, I. S. Member, K. Faez, I. Member, and S. Sargolzaei, “A New Robust Wavelet Based Algorithm for Baseline Wandering Cancellation in ECG Signals,” Electrical Engineering, pp. 33–38, 2009.10.1109/ICSIPA.2009.5478671
  14. a G. Ramakrishnan and S. Saha, “ECG coding by wavelet-based linear prediction.,” IEEE transactions on bio-medical engineering, vol. 44, no. 12, pp. 1253–61, Dec. 1997.
  15. Z. Zhao and Y. Chen, “A NEW METHOD FOR REMOVAL OF BASELINE WANDER AND POWER,” Machine Learning, no. August, pp. 13–16, 2006.10.1109/ICMLC.2006.259082
  16. O. Pahlm and L. Sörnmo, “Software QRS detection in ambulatory monitoring — a review,” Medical& Biological Engineering& Computing, vol. 22, no. 4, pp. 289–297, Jul. 1984.10.1007/BF024420956379330
  17. J. Pan and W. . Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, vol. BME-32, pp. 230–236, 1985.10.1109/TBME.1985.3255323997178
  18. A. Alshamali, T. Ghaith, H. Faculty, and Y. Universitye, “Combined Coding and Adaptive Thresholding Algorithms for ECG Compression,” Database, pp. 2–4.
  19. R. Kazbunda, “Sleep Stages& Apnea Estimation using Electrocardiogram Signal,” Sleep (Rochester), no. October, pp. 1–44, 2006.
  20. T. Suzuki, K. Ouchi, K. Kameyama, and M. Takahashi,“DEVELOPMENT OF A SLEEP MONITORING SYSTEM WITH,” Safety And Health.
  21. A. Rechtschaffen and A. Kales, “A Manual of Standardized Terminology , Techniques and Scoring System for Sleep Stages of Human Subject,” Sleep (Rochester), 1967.
  22. S. Redmond and C. Heneghan, “Electrocardiogram-Based Automatic Sleep Staging in Sleep Disordered Breathing,” System.
  23. D. Ge, N. Srinivasan, and S. M. Krishnan, “Cardiac arrhythmia classification using autoregressive modeling.,” Biomedical engineering online, vol. 1, p. 5, Nov. 2002.10.1186/1475-925X-1-5
  24. M. G. Tsipouras, D. I. Fotiadis, and D. Sideris, “An arrhythmia classification system based on the RR-interval signal.,” Artificial intelligence in medicine, vol. 33, no. 3, pp. 237–50, Mar. 2005.10.1016/j.artmed.2004.03.00715811788
  25. R. Benzid, F. Marir, A. Boussaad, M. Benyoucef, and D. Arar, “Fixed percentage of wavelet coefficients to be zeroed for ECG compression,” Electronics Letters, vol. 39, no. 11, p. 830, 2003.10.1049/el:20030560
  26. M. Blanco-Velasco, F. Cruz-Roldan, J. I. Godino-Llorente, and K. E. Barner, “ECG compression with retrieved quality guaranteed,” vol. 40, no. 23, 2004.10.1049/el:20046382
  27. M. Bsoul, H. Minn, M. Nourani, G. Gupta, and L. Tamil, “Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier.,” Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society. Conference, vol. 2010, pp. 1178–81, Jan. 2010.
  28. Y. Zigel, A. Cohen, and A. Katz, “The weighted diagnostic distortion (WDD) measure for ECG signal compression.,” IEEE transactions on bio-medical engineering, vol. 47, no. 11, pp. 1424–30, Nov. 2000.
  29. M. I. Tawakal, M. E. Suryana, A. Noviyanto, and I. P. Satwika, “Analysis of Multi Codebook GLVQ versus Standard GLVQ in Discriminating Sleep Stages,” in Proceeding of International Conference on Advanced Computer Science and Information Systems, 2012.
  30. [30]A. Noviyanto, S. M. Isa, I. Wasito, and A. M. Arymurthy, “Selecting Features of Single Lead ECG Signal for Automatic Sleep Stages Classification using Correlation-based Feature Subset Selection,” International Journal of Computer Science Issues, vol. 8, no. 5, pp. 139–148, 2011.
  31. M. A. Akbar, M. EkaSuryana, and I. M. Agus, “Modified Fuzzy-Neuro Generalized Learning Vector Quantization for Early Detection of Arrhytmias,” Proceeding of International Conference on Advanced Computer Science and Information Systems, 2012.
Language: English
Page range: 2011 - 2039
Submitted on: Jul 12, 2013
Accepted on: Nov 3, 2013
Published on: Dec 16, 2013
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2013 Sani Muhamad Isa, M. Eka Suryana, M. Ali Akbar, Ary Noviyanto, Wisnu Jatmiko, Aniati Murni Arymurthy, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.