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Adaptive Sampling for ECG Detection Based on Compression Dictionary

  • Yuan, Zhongyun (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Kim, Jong Hak (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Cho, Jun Dong (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • Received : 2013.04.03
  • Accepted : 2013.10.01
  • Published : 2013.12.31

Abstract

This paper presents an adaptive sampling method for electrocardiogram (ECG) signal detection. First, by employing the strings matching process with compression dictionary, we recognize each segment of ECG with different characteristics. Then, based on the non-uniform sampling strategy, the sampling rate is determined adaptively. As the results of simulation indicated, our approach reconstructed the ECG signal at an optimized sampling rate with the guarantee of ECG integrity. Compared with the existing adaptive sampling technique, our approach acquires an ECG signal at a 30% lower sampling rate. Finally, the experiment exhibits its superiority in terms of energy efficiency and memory capacity performance.

Keywords

References

  1. S. Feizi, G. Angelopoulos, V. K. Goyal, and M. Medard, "Energy-Efficient Time-Stampless Adaptive Nonuniform Sampling," Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process., 3809-3812, 2012.
  2. H. J. Kim, C. V. Hoof, and R. F. Yazicioglu, "A Mixed Signal ECG Processing Platform with an Adaptive Sampling ADC for Portable Monitoring Application," 33rd annual international conference of the IEEE EMBS., 2196-2199, 2011.
  3. P.Augustyniak, "ECG Sampling Rate Controlled by Signal Contents," 4th Symbiosis, 128- 131, 2001.
  4. D. Konstantas, A. V. Halteren1, R. Bults1, K.Wac, and R.Herzog, "Mobihealth: Ambulant Patient Monitoring Over Public Wireless Networks," Mediterranean Conference on Medical and Biological Engineering MEDICO., 107-122, 2004.
  5. C. R. Baker, K. Armijo, S. Belka, M. Benhabib, V. Bhargava, and N. Burkhart, "Wireless Sensor Networks for Home Health Care," 21st International Conference on Advanced Information Networking and Applications Workshops, 832-837, 2007.
  6. Rebizant, Waldemar, Szafran, Janusz, Wiszniewski, and Andrzej, "Digital Signal Processing in Power System Protection and Control," SBN 978-0-85729- 8010, 2011.
  7. V. Almenar and A. Albiol, "A New Adaptive Scheme for ECG Enhancement," Signal Processing., 253-263, 1999.
  8. L. Chan and C. L. Wang, "VLSI Implementation of Wavelet-based Electrocardiogram Compression and Decompression " Journal of Medical and Biological Engineering, 331-338, 2011.
  9. C. Alippi, G. Anastasi, M. D. Francesco, and M. Roveri, "An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sensor," Instrumentation and Measurement., 335-344, 2010.
  10. F. Chen, F. Wen, and H. D. Jia, "Algorithm of Data Compression Based on Multiple Principal Component Analysis over the WSN," 6th Wireless Communications Networking and Mobile Computing., 1-4, , 2010.
  11. Zhou Yanli, Fan Xiaoping, Liu Shaoqiang, and Xiong Zheyuan, "Improved LZW Algorithm of Lossless Data Compression for WSN," 3rd Computer Science and Information Technology, 523-527, 2010.
  12. Huan Zhang, Xiaoping Fan, Shaoqiang Liu, and Zhi Zhong, "Design and Realization of Improved LZW Algorithm for Wireless Sensor Networks," Information Science and Technology Conference., 671-675, 2011.
  13. P. Smith, "Comparisons between Low Power Wireless Technologies, Bluetooth low energy, ANT, ANT+, RF4CE, ZigBee, WiFi, Nike+, IrDA and NFC," HBU Marketing document, CSR plc, 2011.
  14. D. Salomon, "Data Comparisons," 2nd Edition, Springer, New York, 2000.
  15. J. Lim, Y. Cho, and J. Choi, "A 9- bit ADC with a Wide-Range Sample-and-Hold Amplifier," Journal of Semiconductor Technology and Science., Vol.4, 280-285, 2004.
  16. S. Nakaya and Y. Nakamura "Adaptive Sensing of ECG Signals using R-R Interval Prediction," 35th Annual International Conference of the IEEE EMBS., 9-12, 2013.