Research on adaptive pulse signal extraction algorithm based on fingertip video image_Journal of Biomedical Engineering

Authors：

YU Jiangjun ^1,2 ,  ZHOU Liang ¹ , LIU Zhaohui ¹ , LI Zhiguo ¹ , SHAN Qiusha ¹

1. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;

Corresponding author：

ZHOU Liang, Email: zhouliang@opt.ac.cn

Keywords：

video image processing; pulse signal extraction; heart rate detection; smartphone

DOI：

10.7507/1001-5515.201901038

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to solve the saturation distortion phenomenon of R component in fingertip video image, this paper proposes an iterative threshold segmentation algorithm, which adaptively generates the region to be detected for the R component, and extracts the human pulse signal by calculating the gray mean value of the region to be detected. The original pulse signal has baseline drift and high frequency noise. Combining with the characteristics of pulse signal, a zero phase digital filter is designed to filter out noise interference. Fingertip video images are collected on different smartphones, and the region to be detected is extracted by the algorithm proposed in this paper. Considering that the fingertip’s pressure will be different during each measurement, this paper makes a comparative analysis of pulse signals extracted under different pressures. In order to verify the accuracy of the algorithm proposed in this paper in heart rate detection, a comparative experiment of heart rate detection was conducted. The results show that the algorithm proposed in this paper can accurately extract human heart rate information and has certain portability, which provides certain theoretical help for further development of physiological monitoring application on smartphone platform.

Citation： YU Jiangjun, ZHOU Liang, LIU Zhaohui, LI Zhiguo, SHAN Qiusha. Research on adaptive pulse signal extraction algorithm based on fingertip video image. Journal of Biomedical Engineering, 2020, 37(1): 150-157. doi: 10.7507/1001-5515.201901038 Copy

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10.	Kong Lingqin, Zhao Yuejin, Dong Liquan, et al. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light. Opt Express, 2013, 21(15): 17464-17471.
11.	Fan Q, Li K. Non-contact imaging plethysmography for accurate estimation of physiological parameters. J Med Biol Eng, 2017, 37(5): 675-685.
12.	Chu C H, Delp E J. Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators. IEEE Trans Biomed Eng, 1989, 36(2): 262-273.
13.	Mandic D P, Rehman N U, Wu Z, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Signal Process Mag, 2013, 30(6): 74-86.
14.	Aguiar-Conraria L, Soares M J. The continuous wavelet transform: moving beyond uni-and bivariate analysis. J Econ Surv, 2014, 28(2): 344-375.
15.	Lai Xiaoping, Lin Zhiping. Minimax phase error design of IIR digital filters with prescribed magnitude and phase responses. IEEE Trans Signal Process, 2012, 60(2): 980-986.

1. 陈伟伟, 高润霖, 刘力生, 等. 《中国心血管病报告 2017》概要. 中国循环杂志, 2018, 33(1): 1-8.
2. Boulos M N, Wheeler S, Tavares C, et al. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online, 2011, 10(1): 24.
3. Pelegris P, Banitsas K, Orbach T, et al. A novel method to detect Heart Beat Rate using a mobile phone// Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2010: 5488-5491.
4. Jonathan E, Leahy M J. Cellular phone-based photoplethysmographic imaging. J Biophotonics, 2011, 4(5): 293-296.
5. Karlen W, Ansermino J M, Dumont G A, et al. Detection of the optimal region of interest for camera oximetry. Conf Proc IEEE Eng Med Biol Soc, 2013, 2013: 2263-2266.
6. Scully C G, Lee J, Meyer J, et al. Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng, 2012, 59(2): 303-306.
7. Nam Y, Kong Y, Reyes B, et al. Monitoring of heart and breathing rates using dual cameras on a smartphone. PLoS One, 2016, 11(3): e0151013.
8. Lenskiy A A, Aitzhan Y. Extracting heart rate variability from a smartphone camera. J Inf Commun Converg Eng, 2013, 11(3): 216-222.
9. Lamonaca F, Kurylyak Y, Grimaldi D, et al. Reliable pulse rate evaluation by smartphone// IEEE International Symposium on Medical measurements and Applications Proceedings. Piscataway: IEEE, 2012: 234-237.
10. Kong Lingqin, Zhao Yuejin, Dong Liquan, et al. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light. Opt Express, 2013, 21(15): 17464-17471.
11. Fan Q, Li K. Non-contact imaging plethysmography for accurate estimation of physiological parameters. J Med Biol Eng, 2017, 37(5): 675-685.
12. Chu C H, Delp E J. Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators. IEEE Trans Biomed Eng, 1989, 36(2): 262-273.
13. Mandic D P, Rehman N U, Wu Z, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Signal Process Mag, 2013, 30(6): 74-86.
14. Aguiar-Conraria L, Soares M J. The continuous wavelet transform: moving beyond uni-and bivariate analysis. J Econ Surv, 2014, 28(2): 344-375.
15. Lai Xiaoping, Lin Zhiping. Minimax phase error design of IIR digital filters with prescribed magnitude and phase responses. IEEE Trans Signal Process, 2012, 60(2): 980-986.

Journal of Biomedical Engineering

Research on adaptive pulse signal extraction algorithm based on fingertip video image

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