Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and stationary wavelet transform_Journal of Biomedical Engineering

Authors：

LIU Jinzhen ^1,2 , SUN Lifei ^1,2 ,  XIONG Hui ^1,2 , LIANG Meiling ³

1. School of Electrical Engineering and Automation, TianGong University, Tianjin 300387, P.R.China;
2. Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, TianGong University, Tianjin 300387, P.R.China;
3. Beijing Zhaoyi Innovation Technology Co., Ltd, Beijing 100088, P.R.China;

Corresponding author：

XIONG Hui, Email: xionghui@tiangong.edu.cn

Keywords：

exercise electrocardiogram; QRS detection; energy segmentation; stationary wavelet transform

DOI：

10.7507/1001-5515.202002038

Video：

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Abstract Full text Figures/Tables Video References Cited by

The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.

Citation： LIU Jinzhen, SUN Lifei, XIONG Hui, LIANG Meiling. Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and stationary wavelet transform. Journal of Biomedical Engineering, 2021, 38(6): 1181-1192. doi: 10.7507/1001-5515.202002038 Copy

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2.	Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
3.	Elgendi M. Fast QRS detection with an optimized knowledge-based method: evaluation on 11 standard ECG databases. PLoS One, 2013, 8(9): e73557.
4.	Li H Q, Wang X F. Detection of electrocardiogram characteristic points using lifting wavelet transform and hilbert transform. Transactions of the Institute of Measurement and Control, 2013, 35(5): 574-582.
5.	Nguyen T, Qin X, Dinh A, et al. Low resource complexity R-peak detection based on triangle template matching and moving average filter. Sensors, 2019, 19(18): 3997.
6.	金林鹏, 董军. 基于集成学习的临床心电图分类算法研究. 生物医学工程学杂志, 2016, 33(5): 825-833.
7.	Lee J S, Lee S J, Choi M, et al. QRS detection method based on fully convolutional networks for capacitive electrocardiogram. Expert Syst Appl, 2019, 134: 66-78.
8.	Lee J W, Kim K S, Lee B, et al. A real time QRS detection using delay-coordinate mapping for the microcontroller implementation. Ann Biomed Eng, 2002, 30(9): 1140-1151.
9.	Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng, 2007, 54(1): 174-177.
10.	Cvikl M, Jager F, Zemva A. Hardware implementation of a modified delay-coordinate mapping-based QRS complex detection algorithm. EURASIP J Adv Signal Process, 2007, 2007(1): 057286.
11.	Adnane M, Jiang Z, Choi S. Development of QRS detection algorithm designed for wearable cardiorespiratory system. Comput Methods Programs Biomed, 2009, 93(1): 20-31.
12.	Gutiérrez-Rivas R, García J J, Marnane W P, et al. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J, 2015, 15(10): 6036-6043.
13.	Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
14.	Raj S, Ray K C, Shankar O. Development of robust, fast and efficient QRS complex detector: a methodological review. Australas Phys Eng Sci Med, 2018, 41(3): 581-600.
15.	Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994, 81(3): 425-455.
16.	Ingrid D, Lu J, Wu H T. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 2010, 30(2): 243-261.
17.	Sawant C, Patii H T. Wavelet based ECG signal de-noising//2014 First International Conference on Networks & Soft Computing (ICNSC2014), Guntur, India: IEEE, 2014: 20-24.
18.	Li Hongqiang, Wang Xiaofei, Chen Lei, et al. Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Systems and Signal Processing, 2014, 33(4): 1261-1276.
19.	Aiwiscal. ECG×AI: introduction to ECG applications for machine/Deep Learning. (2018-05-16)[2019-12-05]. https://blog.csdn.net/qq_15746879/article/details/80340671.
20.	张恒, 潘仲明. 平稳小波去噪算法中的参数选择. 国防科技大学学报, 2019, 41(4): 165-170.
21.	刘富湘, 莫介超, 陈注有, 等. 丹七片联合美托洛尔治疗冠心病心绞痛的疗效观察. 广东医科大学学报, 2021, 39(4): 466-468.
22.	Qu B, Tao G, Liu R. An emergency ECG sign of ST elevation myocardial infarction. BMC Cardiovasc Disord, 2019, 19(1): 130.
23.	Pierce J B, Rosenthal J, Stone N J. Worth remembering: cardiac memory presenting as deep anterior T-wave inversions explained by intermittent left bundle branch block. Am J Cardiol, 2020, 135: 174-176.
24.	Phukpattaranont P. QRS detection algorithm based on the quadratic filter. Expert Syst Appl, 2015, 42(11): 4867-4877.
25.	Merah M, Abdelmalik T A, Larbi B H. R-peaks detection based on stationary wavelet transform. Comput Methods Programs Biomed, 2015, 121(3): 149-160.
26.	Sharma T, Sharma K K. QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comput Biol Med, 2017, 87: 187-199.
27.	Zidelmal Z, Amirou A, Ould-Abdeslam D, et al. QRS detection using S-transform and Shannon energy. Comput Methods Programs Biomed, 2014, 116(1): 1-9.
28.	Hou Zhongjie, Dong Yonggui, Xiang Jinxi, et al. A real-time QRS detection method based on phase portraits and box-scoring calculation. IEEE Sens J, 2018, 18(9): 3694-3702.
29.	李战明, 杨守祥. 基于平稳小波变换的心电信号P、T波检测算法. 兰州理工大学学报, 2014, 40(3): 85-90.
30.	姚成, 司玉娟, 郎六琪, 等. 基于提升小波的心电信号P、T波检测快速算法. 吉林大学学报:工学版, 2013, 43(S1): 177-182.
31.	熊鹏, 刘学朋, 杜海曼, 等. 基于平稳和连续小波变换融合算法的心电信号P, T波检测. 电子与信息学报, 2021, 43(5): 1441-1447.

1. Josko A. Evaluation of the QRS complex wavelet based detection algorithm. Prz Elektrotechniczn, 2011, 87(5): 66-70.
2. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
3. Elgendi M. Fast QRS detection with an optimized knowledge-based method: evaluation on 11 standard ECG databases. PLoS One, 2013, 8(9): e73557.
4. Li H Q, Wang X F. Detection of electrocardiogram characteristic points using lifting wavelet transform and hilbert transform. Transactions of the Institute of Measurement and Control, 2013, 35(5): 574-582.
5. Nguyen T, Qin X, Dinh A, et al. Low resource complexity R-peak detection based on triangle template matching and moving average filter. Sensors, 2019, 19(18): 3997.
6. 金林鹏, 董军. 基于集成学习的临床心电图分类算法研究. 生物医学工程学杂志, 2016, 33(5): 825-833.
7. Lee J S, Lee S J, Choi M, et al. QRS detection method based on fully convolutional networks for capacitive electrocardiogram. Expert Syst Appl, 2019, 134: 66-78.
8. Lee J W, Kim K S, Lee B, et al. A real time QRS detection using delay-coordinate mapping for the microcontroller implementation. Ann Biomed Eng, 2002, 30(9): 1140-1151.
9. Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng, 2007, 54(1): 174-177.
10. Cvikl M, Jager F, Zemva A. Hardware implementation of a modified delay-coordinate mapping-based QRS complex detection algorithm. EURASIP J Adv Signal Process, 2007, 2007(1): 057286.
11. Adnane M, Jiang Z, Choi S. Development of QRS detection algorithm designed for wearable cardiorespiratory system. Comput Methods Programs Biomed, 2009, 93(1): 20-31.
12. Gutiérrez-Rivas R, García J J, Marnane W P, et al. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J, 2015, 15(10): 6036-6043.
13. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
14. Raj S, Ray K C, Shankar O. Development of robust, fast and efficient QRS complex detector: a methodological review. Australas Phys Eng Sci Med, 2018, 41(3): 581-600.
15. Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994, 81(3): 425-455.
16. Ingrid D, Lu J, Wu H T. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 2010, 30(2): 243-261.
17. Sawant C, Patii H T. Wavelet based ECG signal de-noising//2014 First International Conference on Networks & Soft Computing (ICNSC2014), Guntur, India: IEEE, 2014: 20-24.
18. Li Hongqiang, Wang Xiaofei, Chen Lei, et al. Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Systems and Signal Processing, 2014, 33(4): 1261-1276.
19. Aiwiscal. ECG×AI: introduction to ECG applications for machine/Deep Learning. (2018-05-16)[2019-12-05]. https://blog.csdn.net/qq_15746879/article/details/80340671.
20. 张恒, 潘仲明. 平稳小波去噪算法中的参数选择. 国防科技大学学报, 2019, 41(4): 165-170.
21. 刘富湘, 莫介超, 陈注有, 等. 丹七片联合美托洛尔治疗冠心病心绞痛的疗效观察. 广东医科大学学报, 2021, 39(4): 466-468.
22. Qu B, Tao G, Liu R. An emergency ECG sign of ST elevation myocardial infarction. BMC Cardiovasc Disord, 2019, 19(1): 130.
23. Pierce J B, Rosenthal J, Stone N J. Worth remembering: cardiac memory presenting as deep anterior T-wave inversions explained by intermittent left bundle branch block. Am J Cardiol, 2020, 135: 174-176.
24. Phukpattaranont P. QRS detection algorithm based on the quadratic filter. Expert Syst Appl, 2015, 42(11): 4867-4877.
25. Merah M, Abdelmalik T A, Larbi B H. R-peaks detection based on stationary wavelet transform. Comput Methods Programs Biomed, 2015, 121(3): 149-160.
26. Sharma T, Sharma K K. QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comput Biol Med, 2017, 87: 187-199.
27. Zidelmal Z, Amirou A, Ould-Abdeslam D, et al. QRS detection using S-transform and Shannon energy. Comput Methods Programs Biomed, 2014, 116(1): 1-9.
28. Hou Zhongjie, Dong Yonggui, Xiang Jinxi, et al. A real-time QRS detection method based on phase portraits and box-scoring calculation. IEEE Sens J, 2018, 18(9): 3694-3702.
29. 李战明, 杨守祥. 基于平稳小波变换的心电信号P、T波检测算法. 兰州理工大学学报, 2014, 40(3): 85-90.
30. 姚成, 司玉娟, 郎六琪, 等. 基于提升小波的心电信号P、T波检测快速算法. 吉林大学学报:工学版, 2013, 43(S1): 177-182.
31. 熊鹏, 刘学朋, 杜海曼, 等. 基于平稳和连续小波变换融合算法的心电信号P, T波检测. 电子与信息学报, 2021, 43(5): 1441-1447.

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