Improved Dynamic Programming in Local Linear Approximation Based on a Template in a Lightweight ECG Signal-Processing Edge Device
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초록
Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need todevelop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of theECG 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 thedisadvantage of high complexity using memoization. In this paper, the calculation area and memory usage areimproved using a linear approximated template. The proposed algorithm reduces the calculation area requiredfor dynamic programming through local optimization around the vertices of the template. In addition, itminimizes the storage space required by expressing the time information using the error from the vertices ofthe template, which is more compact than the time difference between vertices. When the length of the signalis 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 msto 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square differenceconfirms that the proposed algorithm is a stable approximation.
Abstract
Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need todevelop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of theECG 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 thedisadvantage of high complexity using memoization. In this paper, the calculation area and memory usage areimproved using a linear approximated template. The proposed algorithm reduces the calculation area requiredfor dynamic programming through local optimization around the vertices of the template. In addition, itminimizes the storage space required by expressing the time information using the error from the vertices ofthe template, which is more compact than the time difference between vertices. When the length of the signalis 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 msto 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square differenceconfirms that the proposed algorithm is a stable approximation.
참고문헌 (25)
- S. L. Rohit, "IoT based health monitoring system using raspberry PI-review", Proceedings of 2018 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), pp.997~1002 (2018)
- Sid Ahmed Elhannachi, "Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods", JIPS(Journal of Information Processing Systems), 13(1), pp.40~56 (2017), 한국정보처리학회[DOI]
- Hana Rhim, "A multi-hop graph-based approach for an energy-efficient routing protocol in wireless sensor networks", Human-centric Computing and Information Sciences, 8(1) (2018), Springer Science and Business Media LLC[DOI]
- Yao Meng, "Health and Wellness Monitoring Using Intelligent Sensing Technique", JIPS(Journal of Information Processing Systems), 15(3), pp.478~491 (2019), 한국정보처리학회[DOI]
- Won Min Kang, "An enhanced security framework for home appliances in smart home", Human-centric Computing and Information Sciences, 7(1) (2017), Springer Science and Business Media LLC[DOI]
- 이우식, "An Adaptive Transmission Power Control Algorithm for Wearable Healthcare Systems Based on Variations in the Body Conditions", JIPS(Journal of Information Processing Systems), 15(3), pp.593~603 (2019), 한국정보처리학회[DOI]
- Teruhiko Teraoka, "Organization and exploration of heterogeneous personal data collected in daily life", Human-centric Computing and Information Sciences, 2(1) (2012), Springer Science and Business Media LLC[DOI]
- Tae-Hun Kim, "Curvature Based ECG Signal Compression for Effective Communication on WPAN", Journal of Communications and Networks, 14(1), pp.21~26 (2012), 한국통신학회
- H. Mamaghanian, "Compressed sensing for real-time energyefficient ECG compression on wireless body sensor nodes", IEEE Transactions on Biomedical Engineering, 58(9), pp.2456~2466 (2011)
- R. Bellman, "Applied Dynamic Programming" (2015), Princeton University Press
- Seungmin Lee, "Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation", Sensors, 18(12), pp.4502~ (2018), MDPI AG[DOI]
- S. Lee, "Advanced real-time dynamic programming in the polygonal approximation of ECG signals for a lightweight embedded device", IEEE Access, 7, pp.162850~162861 (2019)
- S. Lee, "Enhanced dynamic programming for polygonal approximation of ECG signals", Proceedings of 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp.121~122 (2020)
- M. S. Manikandan, "Quality controlled wavelet compression of ECG signals by WEDD", Proceedings of International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), pp.581~586 (2007)
- G. B. Moody, "The MIT-BIH arrhythmia database on CD-ROM and software for use with it", Proceedings of Computers in Cardiology Conference, pp.185~188 (1990)
- M. Merone, "ECG databases for biometric systems : a systematic review", Expert Systems with Applications, 67, pp.189~202 (2017)
- F. A. Elhaj, "Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals", Computer Methods and Programs in Biomedicine, 127, pp.52~63 (2016)
- G. M. Friesen, "A comparison of the noise sensitivity of nine QRS detection algorithms", IEEE Transactions on Biomedical Engineering, 37(1), pp.85~98 (1990)
- S. K. Berkaya, "A survey on ECG analysis", Biomedical Signal Processing and Control, 43, pp.216~235 (2018)
- Alex Pappachen James, "Heart rate monitoring using human speech spectral features", Human-centric Computing and Information Sciences, 5(1) (2015), Springer Science and Business Media LLC[DOI]
- J. Pan, "A real-time QRS detection algorithm", IEEE Transactions on Biomedical Engineering, 32(3), pp.230~236 (1985)
- A. Li, "A novel abnormal ECG beats detection method", Proceedings of 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), pp.47~51 (2010)
- F. Mokhtarian, "Robust image corner detection through curvature scale space", IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), pp.1376~1381 (1998)
- K. J. O'Connell, "Object-adaptive vertex-based shape coding method", IEEE Transactions on Circuits and Systems for Video Technology, 7(1), pp.251~255 (1997)
- Y. Zigel, "The weighted diagnostic distortion(WDD)measure for ECG signal compression", IEEE Transactions on Biomedical Engineering, 47(11), pp.1422~1430 (2000)