Browse > Article
http://dx.doi.org/10.3745/JIPS.03.0173

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)
Publication Information
Journal of Information Processing Systems / v.18, no.1, 2022 , pp. 97-114 More about this Journal
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
Device Discovery; Partition-Based; RDM;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.
3 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.   DOI
4 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   DOI
5 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.
6 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   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 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   DOI
16 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.
17 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.
18 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.   DOI
19 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.
20 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.   DOI
21 R. Bellman and S. Dreyfus, Applied Dynamic Programming. Princeton, NJ: Princeton University Press, 2015.
22 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   DOI
23 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.   DOI
24 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.
25 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.   DOI