Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm_Journal of Biomedical Engineering

Authors：

JIANG Feng ^1,2,3 , ZHU Zhibin ^1,2,3 , ZHANG Mengge ^1,2,3 , FENG Jingwen ^1,2,3 , XU Yifei ^1,2,3 ,  CHEN Hang ^1,2,3

1. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China;
2. Key lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, P. R. China;
3. Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou 310027, P. R. China;

Corresponding author：

CHEN Hang, Email: ch-sun@263.net

Keywords：

Pulse wave; Signal preprocessing; Design pattern; Pulse wave detection algorithm

DOI：

10.7507/1001-5515.202208034

Video：

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As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.

Citation： JIANG Feng, ZHU Zhibin, ZHANG Mengge, FENG Jingwen, XU Yifei, CHEN Hang. Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm. Journal of Biomedical Engineering, 2023, 40(3): 529-535. doi: 10.7507/1001-5515.202208034 Copy

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2.	Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 2007, 28(3): R1-R39.
3.	张雄伟. 外周动脉疾病无创血流动力学检测技术. 北京: 人民卫生出版社, 2010.
4.	陈婉琳. 基于光电容积脉搏波的全身麻醉阵痛水平监测的研究. 杭州: 浙江大学, 2021.
5.	陈婉琳, 江锋, 陈新忠, 等. 脉搏波形态学研究及其在子痫前期的应用. 天津大学学报: 自然科学与工程技术版, 2019, 52(8): 857861.
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7.	Feng Y, Drzymalski D, Zhao B H, et al. Measurement of area difference ratio of Photoplethysmographic pulse wave in patients with pre-eclampsia. BMC Pregnancy Childbirth, 2018, 18(1): 280.
8.	Jiang F, Chen W, Chen X, et al. The Research of photoplethysmography morphology: distincting preeclampsia with hierarchical area ratio// 2018 IEEE 3rd International Conference on Signal and Image Processing, Shenzhen: IEEE & Southeast University, 2018: 202-206.
9.	Su F, Li Z, Sun X, et al. The pulse wave analysis of normal pregnancy: investigating the gestational effects on photoplethysmographic signals. Biomed Mater Eng, 2014, 24(1): 209-219.
10.	陈沙利. 基于外周生理信号的情绪识别研究. 杭州: 浙江大学, 2021.
11.	马济通. 基于脉搏波的生理参数监测与生理信号重建技术研究. 大连: 大连理工大学, 2015.
12.	缪家俊. 基于FPGA的多生理信号采集与智能分析系统设计. 杭州: 浙江大学, 2020.
13.	Levin R, Cohen E, Corwin W, et al. Policy/mechanism separation in hydra//the Fifth ACM Symposium on Operating Systems Principles, Austin: ACM, 1975: 132–140.
14.	Freeman E, Freeman E, Bates B, et al. Head first design patterns. Sebastopol: O'Reilly Media, 2004.
15.	Gamma E, Helm R, Johnson R, et al. Design patterns: abstraction and reuse of object-oriented design// 1993 European Conference on Object-Oriented Programming, Kaiserslautern: ATIO, 1993: 406-431.
16.	Naraharisetti K V P, Bawa M. Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal//2011 IEEE International Conference on Electro Information Technology, Mankato: IEEE, 2011.
17.	Zhang X, Xu L, Chen K, et al. A new method for locating feature points in pulse wave using wavelet transform// 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles: IEEE, 2009: 367-371.
18.	齐妍妍. 脉搏信号去噪及特征提取方法的研究. 北京: 北京工业大学, 2008.
19.	孙健. 脉搏波特征提取算法及其应用研究. 大连: 大连理工大学, 2007.
20.	周志华. 机器学习. 北京: 清华大学出版社, 2016.
21.	邱天爽, 唐洪, 刘海龙. 统计信号处理: 医学信号分析与处理. 北京: 科学出版社, 2012.
22.	Chen H, Jiang F, Drzymalski D, et al. A novel parameter derived from photoplethysmographic pulse wave to distinguish preeclampsia from non-preeclampsia. Pregnancy Hypertens, 2019, 15:166-170.
23.	Kachuee M, Kiani M M, Mohammadzade H, et al. Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time// IEEE International Symposium on Circuits and Systems, Lisbon: IEEE, 2015: 1006-1009.
24.	Johnson A E, Stone D J, Celi L A, et al. The MIMIC code repository: enabling reproducibility in critical care research. J Am Med Inform Assoc, 2018, 25(1): 32-39.
25.	van Gent P, Haneen F, Nicole V N, et al. HeartPy: a novel heart rate algorithm for the analysis of noisy signals. Transportation Research Part F, 2019, 66: 368-378.
26.	van Gent P, Haneen F, Nicole V N, et al. Analysing noisy driver physiology real-time using off-the-shelf sensors: heart rate analysis software from the taking the fast lane project. Journal of Open Research Software, 2019, 7: 32.

1. 罗志昌, 张松, 杨益民. 脉搏波的工程分析与临床应用. 北京: 科学出版社, 2006.
2. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 2007, 28(3): R1-R39.
3. 张雄伟. 外周动脉疾病无创血流动力学检测技术. 北京: 人民卫生出版社, 2010.
4. 陈婉琳. 基于光电容积脉搏波的全身麻醉阵痛水平监测的研究. 杭州: 浙江大学, 2021.
5. 陈婉琳, 江锋, 陈新忠, 等. 脉搏波形态学研究及其在子痫前期的应用. 天津大学学报: 自然科学与工程技术版, 2019, 52(8): 857861.
6. Solà J, Delgado-Gonzalo R. The handbook of cuffless blood pressure monitoring: a practical guide for clinicians, researchers, and engineers. Switzerland: Springer Cham, 2019.
7. Feng Y, Drzymalski D, Zhao B H, et al. Measurement of area difference ratio of Photoplethysmographic pulse wave in patients with pre-eclampsia. BMC Pregnancy Childbirth, 2018, 18(1): 280.
8. Jiang F, Chen W, Chen X, et al. The Research of photoplethysmography morphology: distincting preeclampsia with hierarchical area ratio// 2018 IEEE 3rd International Conference on Signal and Image Processing, Shenzhen: IEEE & Southeast University, 2018: 202-206.
9. Su F, Li Z, Sun X, et al. The pulse wave analysis of normal pregnancy: investigating the gestational effects on photoplethysmographic signals. Biomed Mater Eng, 2014, 24(1): 209-219.
10. 陈沙利. 基于外周生理信号的情绪识别研究. 杭州: 浙江大学, 2021.
11. 马济通. 基于脉搏波的生理参数监测与生理信号重建技术研究. 大连: 大连理工大学, 2015.
12. 缪家俊. 基于FPGA的多生理信号采集与智能分析系统设计. 杭州: 浙江大学, 2020.
13. Levin R, Cohen E, Corwin W, et al. Policy/mechanism separation in hydra//the Fifth ACM Symposium on Operating Systems Principles, Austin: ACM, 1975: 132–140.
14. Freeman E, Freeman E, Bates B, et al. Head first design patterns. Sebastopol: O'Reilly Media, 2004.
15. Gamma E, Helm R, Johnson R, et al. Design patterns: abstraction and reuse of object-oriented design// 1993 European Conference on Object-Oriented Programming, Kaiserslautern: ATIO, 1993: 406-431.
16. Naraharisetti K V P, Bawa M. Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal//2011 IEEE International Conference on Electro Information Technology, Mankato: IEEE, 2011.
17. Zhang X, Xu L, Chen K, et al. A new method for locating feature points in pulse wave using wavelet transform// 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles: IEEE, 2009: 367-371.
18. 齐妍妍. 脉搏信号去噪及特征提取方法的研究. 北京: 北京工业大学, 2008.
19. 孙健. 脉搏波特征提取算法及其应用研究. 大连: 大连理工大学, 2007.
20. 周志华. 机器学习. 北京: 清华大学出版社, 2016.
21. 邱天爽, 唐洪, 刘海龙. 统计信号处理: 医学信号分析与处理. 北京: 科学出版社, 2012.
22. Chen H, Jiang F, Drzymalski D, et al. A novel parameter derived from photoplethysmographic pulse wave to distinguish preeclampsia from non-preeclampsia. Pregnancy Hypertens, 2019, 15:166-170.
23. Kachuee M, Kiani M M, Mohammadzade H, et al. Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time// IEEE International Symposium on Circuits and Systems, Lisbon: IEEE, 2015: 1006-1009.
24. Johnson A E, Stone D J, Celi L A, et al. The MIMIC code repository: enabling reproducibility in critical care research. J Am Med Inform Assoc, 2018, 25(1): 32-39.
25. van Gent P, Haneen F, Nicole V N, et al. HeartPy: a novel heart rate algorithm for the analysis of noisy signals. Transportation Research Part F, 2019, 66: 368-378.
26. van Gent P, Haneen F, Nicole V N, et al. Analysing noisy driver physiology real-time using off-the-shelf sensors: heart rate analysis software from the taking the fast lane project. Journal of Open Research Software, 2019, 7: 32.

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