Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Improved Dynamic Programming in Local Linear Approximation Based on a Template in a Lightweight ECG Signal-Processing Edge Device

  • Lee, Seungmin (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Park, Daejin (School of Electronic and Electrical Engineering, Kyungpook National University)
  • Received : 2020.09.15
  • Accepted : 2020.12.01
  • Published : 2022.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

Keywords

Acknowledgement

This study was supported by the BK21 FOUR project funded by the Ministry of Education, Korea (No. 4199990113966, 10%), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. NRF-2019R1A2C2005099, 10%), and Ministry of Education (No. NRF-2018R1A6A1A03025109, 10%; No. NRF-2020R1I1A1A01072343, 10%), and Institute of Information & communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00944, Metamorphic approach of unstructured validation/verification for analyzing binary code, 60%), and the EDA tool was supported by the IC Design Education Center (IDEC), Korea.

References

  1. S. L. Rohit and B. V. Tank, "IoT based health monitoring system using raspberry PI-review," in Proceedings of 2018 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 997-1002.
  2. S. A. Elhannachi, N. Benamrane, and T. A. Abdelmalik, "Adaptive medical image compression based on lossy and lossless embedded zerotree methods," Journal of Information Processing Systems, vol. 13, no. 1, pp. 40-56, 2017. https://doi.org/10.3745/JIPS.02.0052
  3. H. Rhim, K. Tamine, R. Abassi, D. Sauveron, and S. Guemara, "A multi-hop graph-based approach for an energy-efficient routing protocol in wireless sensor networks," Human-centric Computing and Information Sciences, vol. 8, article no. 30, 2018. https://doi.org/10.1186/s13673-018-0153-6
  4. Y. Meng, S. H. Yi, and H. C. Kim, "Health and wellness monitoring using intelligent sensing technique," Journal of Information Processing Systems, vol. 15, no. 3, pp. 478-491, 2019. https://doi.org/10.3745/JIPS.04.0115
  5. W. M. Kang, S. Y. Moon, and J. H. Park, "An enhanced security framework for home appliances in smart home," Human-centric Computing and Information Sciences, vol. 7, article no. 6, 2017. https://doi.org/10.1186/s13673-017-0087-4
  6. W. Lee, N. Kim, and B. D. Lee, "An adaptive transmission power control algorithm for wearable healthcare systems based on variations in the body conditions," Journal of Information Processing Systems, vol. 15, no. 3, pp. 593-603, 2019. https://doi.org/10.3745/JIPS.03.0118
  7. T. Teraoka, "Organization and exploration of heterogeneous personal data collected in daily life," Human- Centric Computing and Information Sciences, vol. 2, article no. 1, 2012. https://doi.org/10.1186/2192-1962-2-1
  8. T. H. Kim, S. Y. Kim, J. H. Kim, B. J. Yun, and K. H. Park, "Curvature based ECG signal compression for effective communication on WPAN," Journal of Communications and Networks, vol. 14, no. 1, pp. 21-26, 2012. https://doi.org/10.1109/JCN.2012.6184547
  9. H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, "Compressed sensing for real-time energyefficient ECG compression on wireless body sensor nodes," IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2456-2466, 2011. https://doi.org/10.1109/TBME.2011.2156795
  10. R. Bellman and S. Dreyfus, Applied Dynamic Programming. Princeton, NJ: Princeton University Press, 2015.
  11. S. Lee, Y. Jeong, D. Park, B. J. Yun, and K. H. Park, "Efficient fiducial point detection of ECG QRS complex based on polygonal approximation," Sensors, vol. 18, no. 12, article no. 4502, 2018. https://doi.org/10.3390/s18124502
  12. S. Lee, Y. Jeong, J. Kwak, D. Park, and K. H. Park, "Advanced real-time dynamic programming in the polygonal approximation of ECG signals for a lightweight embedded device," IEEE Access, vol. 7, pp. 162850-162861, 2019. https://doi.org/10.1109/access.2019.2952399
  13. S. Lee and D. Park, "Enhanced dynamic programming for polygonal approximation of ECG signals," in Proceedings of 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), Kyoto, Japan, 2020, pp. 121-122.
  14. M. S. Manikandan and S. Dandapat, "Quality controlled wavelet compression of ECG signals by WEDD," in Proceedings of International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sivakasi, India, 2007, pp. 581-586.
  15. G. B. Moody and R. G. Mark, "The MIT-BIH arrhythmia database on CD-ROM and software for use with it," in Proceedings of Computers in Cardiology Conference, Chicago, IL, 1990, pp. 185-188.
  16. M. Merone, P. Soda, M. Sansone, and C. Sansone, "ECG databases for biometric systems: a systematic review," Expert Systems with Applications, vol. 67, pp. 189-202, 2017. https://doi.org/10.1016/j.eswa.2016.09.030
  17. F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee, and T. Ahmed, "Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals," Computer Methods and Programs in Biomedicine, vol. 127, pp. 52-63, 2016. https://doi.org/10.1016/j.cmpb.2015.12.024
  18. G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, "A comparison of the noise sensitivity of nine QRS detection algorithms," IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85-98, 1990. https://doi.org/10.1109/10.43620
  19. S. K. Berkaya, A. K. Uysal, E. S. Gunal, S. Ergin, S. Gunal, and M. B. Gulmezoglu, "A survey on ECG analysis," Biomedical Signal Processing and Control, vol. 43, pp. 216-235, 2018. https://doi.org/10.1016/j.bspc.2018.03.003
  20. A. P. James, "Heart rate monitoring using human speech spectral features," Human-centric Computing and Information Sciences, vol. 5, article no. 30, 2015. https://doi.org/10.1186/s13673-015-0052-z
  21. J. Pan and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230-236, 1985.
  22. A. Li, S. Wang, H. Zheng, L. Ji, and J. Wu, "A novel abnormal ECG beats detection method," in Proceedings of 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, 2010, pp. 47-51.
  23. F. Mokhtarian and R. Suomela, "Robust image corner detection through curvature scale space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1376-1381, 1998. https://doi.org/10.1109/34.735812
  24. K. J. O'Connell, "Object-adaptive vertex-based shape coding method," IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 1, pp. 251-255, 1997. https://doi.org/10.1109/76.554440
  25. Y. Zigel, A. Cohen, and A. Katz, "The weighted diagnostic distortion (WDD) measure for ECG signal compression," IEEE Transactions on Biomedical Engineering, vol. 47, no. 11, pp. 1422-1430, 2000. https://doi.org/10.1109/tbme.2000.880093