A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts

  • Bo Pang
  • Ming Liu
  • Xu Zhang
  • Peng Li
  • Hongda Chen
Research Paper


One of the most important applications of photoplethysmography (PPG) signal is heartrate (HR) estimation. For its applications in wearable devices, motion artifact (MA) may be the most serious challenge for randomness both in format and temporal distribution. This paper proposes an advanced time-frequency analysis framework based on empirical mode decomposition (EMD) to select specific time slices for signal reconstruction. This framework operates with a type of pre-processing called variance characterization series (VCS), EMD, singular value decomposition (SVD), and a precise and adaptive 2-D filtration reported first. This filtration is based on Harr wavelet transform (HWT) and 3rd order cumulant analysis, to make it have resolution in both the time domain and different components. The simulation results show that the proposed method gains 1.07 in absolute average error (AAE) and 1.87 in standard deviation (SD); AAEs’ 1st and 3rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the PhysioBank MIMIC II waveform database.


photoplethysmography (PPG) motion artifact empirical mode decomposition (EMD) singular value decomposition discrete wavelet transform higher-order statistics 



This work was supported by National Natural Science Foundation of China (Grant Nos. 61634006, 61372060, 61335010, 61474107, 81300803), National Key Technologies R&D Program (Grant Nos. 2016YFB0401303, 2016YFB0402405), Basic Research Project of Shanghai Science and Technology Commission (Grant No. 16JC1400101), and Key Research Program of Frontier Science, Chinese Academy of Sciences (Grant No. QYZDY-SSW-JSC004).


  1. 1.
    Selvaraj N, Jaryal A, Santhosh J, et al. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. J Med Eng Technol, 2008, 32: 479–484CrossRefGoogle Scholar
  2. 2.
    Petersen C L, Chen T P, Ansermino J M, et al. Design and evaluation of a low-cost smartphone pulse oximeter. Sensors, 2013, 13: 16882–16893CrossRefGoogle Scholar
  3. 3.
    Lee J, Jung W. Design the filter to reject motion artifact of pulse oximetry. Elsevier Comput Sci, 2003, 26: 241–249Google Scholar
  4. 4.
    Zhang Z, Pi Z, Liu B. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans Biomed Eng, 2015, 62: 522–531CrossRefGoogle Scholar
  5. 5.
    Murthy N K L, Madhusudana P C, Suresha P, et al. Multiple spectral peak tracking for heart rate monitoring from photoplethysmography signal during intensive physical exercise. IEEE Signal Process Lett, 2015, 22: 2391–2395CrossRefGoogle Scholar
  6. 6.
    Komaty A, Boudraa A O, Augier B, et al. EMD-based filtering using similarity measure between probability density functions of IMFs. IEEE Trans Instrum Meas, 2014, 63: 27–34CrossRefGoogle Scholar
  7. 7.
    Yang G, Liu Y, Wang Y, et al. EMD interval thresholding denoising based on similarity measure to select relevant modes. Signal Process, 2015, 109: 95–109CrossRefGoogle Scholar
  8. 8.
    Sung P, Syed Z, Guttag J. Quantifying morphology changes in time series data with skew. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 2009. 477–480Google Scholar
  9. 9.
    Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1: 1–41CrossRefGoogle Scholar
  10. 10.
    Motin M A, Karmakar C, Palaniswami M. Ensemble empirical mode decomposition with principal component analysis: a novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal. IEEE J Biomed Health Inf, 2017. doi: 10.1109/JBHI.2017.2679108Google Scholar
  11. 11.
    Pang B, Liu M, Zhang X, et al. Advanced EMD method using variance characterization for PPG with motion artifact. In: Proceedings of 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), Shanghai, 2016. 196–199CrossRefGoogle Scholar
  12. 12.
    Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Math Phys Eng Sci, 1998, 454: 903–995CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Li P, Liu M, Zhang X, et al. A low-complexity ECG processing algorithm based on the Haar wavelet transform for portable health-care devices. Sci China Inf Sci, 2014, 57: 122303Google Scholar
  14. 14.
    Gao Y, Ge G, Sheng Z, et al. Analysis and solution to the mode mixing phenomenon in EMD. Congress Image Signal Process, 2008, 5: 223–227CrossRefGoogle Scholar
  15. 15.
    Dimitrakopoulos R, Mustapha H, Gloaguen E. High-order statistics of spatial random fields: exploring spatial cumulants for modeling complex non-Gaussian and non-linear phenomena. Math Geosci, 2010, 42: 65–99CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Donoho D L. De-noising by soft-thresholding. IEEE Trans Inf Theory, 1995, 41: 613–627CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Li X, Zhang X, Li P, et al. An 8.12 W wavelet denoising chip for PPG detection and portable heart rate monitoring in 0.18 µm CMOS. J Semicond, 2016, 37: 055006CrossRefGoogle Scholar
  18. 18.
    Zhang Z. Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction. IEEE Trans Biomed Eng, 2015, 62: 1902–1910CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Bo Pang
    • 1
    • 2
  • Ming Liu
    • 1
  • Xu Zhang
    • 1
  • Peng Li
    • 1
  • Hongda Chen
    • 1
  1. 1.State Key Laboratory on Integrated Optoelectronics, Institute of SemiconductorsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations