Skip to main content

Advertisement

SpringerLink
Log in

Photoplethysmograph Signal Reconstruction based on a Novel Motion Artifact Detection-Reduction Approach. Part II: Motion and Noise Artifact Removal

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

We introduce a new method to reconstruct motion and noise artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO2) values recorded by a pulse oximeter can be derived. Using both computer simulations and three different experimental data sets, we show that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO2 values do not significantly deviate from the uncorrupted reference measurements. Comparison of the accuracy of reconstruction of the MNA corrupted data segments between our IMAR approach and the time-domain independent component analysis (TD-ICA) is made for all data sets as the latter method has been shown to provide good performance. For simulated data, there were no significant differences in the reconstructed HR and SpO2 values starting from 10 dB down to −15 dB for both white and colored noise contaminated PPG data using IMAR; for TD-ICA, significant differences were observed starting at 10 dB. Two experimental PPG data sets were created with contrived MNA by having subjects perform random forehead and rapid side-to-side finger movements show that; the performance of the IMAR approach on these data sets was quite accurate as non-significant differences in the reconstructed HR and SpO2 were found compared to non-contaminated reference values, in most subjects. In comparison, the accuracy of the TD-ICA was poor as there were significant differences in reconstructed HR and SpO2 values in most subjects. For non-contrived MNA corrupted PPG data, which were collected with subjects performing walking and stair climbing tasks, the IMAR significantly outperformed TD-ICA as the former method provided HR and SpO2 values that were non-significantly different than MNA free reference values.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. Bishop, G and G. Welch. An Introduction to the Kalman Filter. Tech. Rep, 2006.

  2. Choi, S., A. Cichocki, H. Min Park, and S. Young Lee. Blind source separation and independent component analysis: a review. Neural Inf. Process. Lett. Rev. 6:1–57, 2004.

    Google Scholar 

  3. Chon, K. H., S. Dash, and K. Ju. Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE `Trans. Bio-med. Eng. 56:2054–2063, 2009.

    Article  Google Scholar 

  4. Comon, Pierre. Independent component analysis, a new concept? Signal Process. 36:287–314, 1994.

    Article  Google Scholar 

  5. Diniz, P. Adaptive Filtering: Algorithms and Practical Implementation. Berlin: Springer Science, Business Media L.L.C., 2008.

    Book  Google Scholar 

  6. Elsner, J. B., and A. A. Tsonis. Singular Spectrum Analysis: A New Tool in Time Series Analysis. Berlin: Springer, 1996.

    Book  Google Scholar 

  7. Feleppa, J., and E. J. Mamou. Singular spectrum analysis applied to ultrasonic detection and imaging of brachytherapy seeds. Acoust. Soc. Am. 21:1790–1801, 2007.

    Google Scholar 

  8. Gao, J., C. Zheng, P. Wang. Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis. Clin. EEG Neurosci. 41(1):53–59, 2010.

    Article  PubMed  Google Scholar 

  9. Ghaderi, F., H. R. Mohseni, and S. Sanei. Localizing heart sounds in respiratory signals using singular spectrum analysis. IEEE Trans. Biomed. Eng. 58:3360–3367, 2011.

    Article  PubMed  Google Scholar 

  10. Golyandina, N., V. Nekrutkin, and A. A. Zhigljavsky. Analysis of Time Series Structure: SSA and Related Techniques. London: Taylor & Francis, 2001.

    Book  Google Scholar 

  11. Hamilton, P. S., M. G. Curley, R. M. Aimi, et al. Comparison of methods for adaptive removal of motion artifact. In: Computers in Cardiology 2000, pp. 383–386, 2000.

  12. Hassani, H. Singular spectrum analysis: methodology and comparison. J. Data Sci. 5:239–257, 2007.

    Google Scholar 

  13. Jolliffe, I. T. Principal Component Analysis. Berlin: Springer, 1986.

    Book  Google Scholar 

  14. Jubran A. Pulse oximetry. In: Applied Physiology in Intensive Care Medicine, edited by G. Hedenstierna, J. Mancebo, L. Brochard, et al. Berlin: Springer, pp. 45–48.

  15. Kalman, R. E. A new approach to linear filtering and prediction problems transactions of the ASME. J. Basic Eng. Ser. D 1:2, 1960.

    Google Scholar 

  16. Kim, B. S., and S. K. Yoo. Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Trans. Biomed. Eng. 53:566–568, 2006.

    Article  PubMed  Google Scholar 

  17. Kloeden, P. E., and E. Platen. Numerical Solution of Stochastic Differential Equations. Berlin: Springer, 1992.

    Book  Google Scholar 

  18. Krishnan, R., B. Natarajan, and S. Warren. Two-stage approach for detection and reduction of motion artifacts in photoplethysmographic data. IEEE Trans. Biomed. Eng. 57:1867–1876, 2010.

    Article  PubMed  Google Scholar 

  19. Morbidi, F., A. Garulli, D. Prattichizzo, et al. Application of Kalman filter to remove TMS-induced artifacts from EEG recordings. IEEE Trans. Control Syst. Technol. 16:1360–1366, 2008.

    Article  Google Scholar 

  20. Ram, M. R., K. V. Madhav, E. H. Krishna et al. Use of multi-scale principal component analysis for motion artifact reduction of PPG signals. Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. pp. 425–430, 2011.

  21. Ram, M. R., K. V. Madhav, E. H. Krishna, et al. ICA-based improved DTCWT technique for MA reduction in PPG signals with restored respiratory information. IEEE Trans. Instrum. Meas. 62:2639–2651, 2013.

    Article  Google Scholar 

  22. Reddy, K. A., B. George, N. Madhu Mohan et al. A novel method of measurement of oxygen saturation in arterial blood. Instrumentation and Measurement Technology Conference Proceedings, 2008 IMTC 2008 IEEE, pp. 1627–1630, 2008.

  23. Reddy, K. A., B. George, N. M. Mohan, et al. A novel calibration-free method of measurement of oxygen saturation in arterial blood. IEEE Trans. Instrum. Meas. 58:1699–1705, 2009.

    Article  CAS  Google Scholar 

  24. Seyedtabaii, S. and L. Seyedtabaii. Kalman Filter Based Adaptive Reduction of Motion Artifact from Photoplethysmographic Signal, Vol. 37. World Academy of Science, Engineering and Technology, 2008.

  25. Sweeney, K. T., T. E. Ward, and S. F. Mcloone. Artifact removal in physiological signals—practices and possibilities. IEEE Trans. Inf. Technol. Biomed. 16:488–500, 2012.

    Article  PubMed  Google Scholar 

  26. Teixeira, A. R., A. M. Tomé, E. W. Lang, et al. Automatic removal of high-amplitude artefacts from single-channel electroencephalograms. Comput. Methods Programs Biomed. 83:125–138, 2006.

    Article  PubMed  CAS  Google Scholar 

  27. Thakor, N. V., and Y.-S. Zhu. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38:785–794, 1991.

    Article  PubMed  CAS  Google Scholar 

  28. Thompson, B. Canonical Correlation Analysis: Uses and Interpretation. Thousand Oaks: SAGE Publications, 1984.

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the US Army Medical Research and Materiel Command (USAMRMC) under Grant No. W81XWH-12-1-0541.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609-2280, USA

    S. M. A. Salehizadeh, Duy K. Dao, Jo Woon Chong, Yitzhak Mendelson & Ki H. Chon

  2. Cardiology Division, Departments of Medicine and Quantitative Health Sciences, University of Massachusetts Medical Center, Worcester, MA, 01655, USA

    David McManus

  3. Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Chad Darling

Authors
  1. S. M. A. Salehizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Duy K. Dao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jo Woon Chong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David McManus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chad Darling
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yitzhak Mendelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ki H. Chon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ki H. Chon.

Additional information

Associate Editor Tingrui Pan oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salehizadeh, S.M.A., Dao, D.K., Chong, J.W. et al. Photoplethysmograph Signal Reconstruction based on a Novel Motion Artifact Detection-Reduction Approach. Part II: Motion and Noise Artifact Removal. Ann Biomed Eng 42, 2251–2263 (2014). https://doi.org/10.1007/s10439-014-1030-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-014-1030-8

Keywords

Search

Navigation