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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Abstract Full text Figures/Tables Video References Cited by

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.	張雄偉. 外周動脈疾病無創血流動力學檢測技術. 北京: 人民衛生出版社, 2010.
4.	陳婉琳. 基于光電容積脈搏波的全身麻醉陣痛水平監測的研究. 杭州: 浙江大學, 2021.
5.	陳婉琳, 江鋒, 陳新忠, 等. 脈搏波形態學研究及其在子癇前期的應用. 天津大學學報: 自然科學與工程技術版, 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.	陳沙利. 基于外周生理信號的情緒識別研究. 杭州: 浙江大學, 2021.
11.	馬濟通. 基于脈搏波的生理參數監測與生理信號重建技術研究. 大連: 大連理工大學, 2015.
12.	繆家俊. 基于FPGA的多生理信號采集與智能分析系統設計. 杭州: 浙江大學, 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.	齊妍妍. 脈搏信號去噪及特征提取方法的研究. 北京: 北京工業大學, 2008.
19.	孫健. 脈搏波特征提取算法及其應用研究. 大連: 大連理工大學, 2007.
20.	周志華. 機器學習. 北京: 清華大學出版社, 2016.
21.	邱天爽, 唐洪, 劉海龍. 統計信號處理: 醫學信號分析與處理. 北京: 科學出版社, 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. 羅志昌, 張松, 楊益民. 脈搏波的工程分析與臨床應用. 北京: 科學出版社, 2006.
2. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 2007, 28(3): R1-R39.
3. 張雄偉. 外周動脈疾病無創血流動力學檢測技術. 北京: 人民衛生出版社, 2010.
4. 陳婉琳. 基于光電容積脈搏波的全身麻醉陣痛水平監測的研究. 杭州: 浙江大學, 2021.
5. 陳婉琳, 江鋒, 陳新忠, 等. 脈搏波形態學研究及其在子癇前期的應用. 天津大學學報: 自然科學與工程技術版, 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. 陳沙利. 基于外周生理信號的情緒識別研究. 杭州: 浙江大學, 2021.
11. 馬濟通. 基于脈搏波的生理參數監測與生理信號重建技術研究. 大連: 大連理工大學, 2015.
12. 繆家俊. 基于FPGA的多生理信號采集與智能分析系統設計. 杭州: 浙江大學, 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. 齊妍妍. 脈搏信號去噪及特征提取方法的研究. 北京: 北京工業大學, 2008.
19. 孫健. 脈搏波特征提取算法及其應用研究. 大連: 大連理工大學, 2007.
20. 周志華. 機器學習. 北京: 清華大學出版社, 2016.
21. 邱天爽, 唐洪, 劉海龍. 統計信號處理: 醫學信號分析與處理. 北京: 科學出版社, 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.

Journal of Biomedical Engineering

Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm

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