Pulse wave digital processing and beat detection based on wavelet transform and adaptive thresholding

Aleksandr A. Fedotov (Login required)
Laser and biotechnical systems department, Samara State Aerospace University, Russia

Anna S. Akulova
Laser and biotechnical systems department, Samara State Aerospace University, Russia

Sergey A. Akulov
Laser and biotechnical systems department, Samara State Aerospace University, Russia

Paper #2646 received 2015.10.03; revised manuscript received 2015.11.08; accepted for publication 2015.12.14; published online 2015.12.15.

DOI: 10.18287/jbpe-2015-1-3-193


This paper considers the adaptive algorithm for pulse wave processing in the presence of various physiological interferences, such as motion artefacts and baseline wander. The proposed processing technique consists of two main steps: comprehensive filtering of pulse wave based on wavelet transforms and noise-resistant pulse beats detection based on the set of pass-band filtering, nonlinear transforms and adaptive thresholding. To eliminate baseline wander from pulse wave signal we introduced a new algorithm based on the principles of adaptive noise cancellation, while the reference signal for adaptive filtering was generated by using multiresolution wavelet transform. For reducing motion artefacts and others high frequency distortions of pulse wave we suggested an approach based on soft thresholding of detailed coefficients from wavelet decomposition. The efficiency of the developed algorithm for pulse wave signal processing was assessed in comparison with the existing approaches by using mathematical modeling of pulse wave signal and occurred contaminations. The performance of the pulse beats detector was further verified for different recordings of clinical pulse wave signals from the Physionet database.


pulse wave; wavelet transform; detector; fiducial point; error; motion artefact; baseline wander

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1. J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiological Measurement 28(3), 1-39 (2007).

2. J. G. Webster, Design of Pulse Oximeters. The Medical Science Series, Taylor & Francis (1997). ISBN: 978-0-7503-0467-2.

3. D. P. Jones, “Medical electro-optics: measurements in the human microcirculation,” Physics in Technology 18(2), 79-85 (1987). Crossref

4. L. Xu, D. Zhang, and K. Wang, “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering 52(11), 1973-1975 (2005). Crossref

5. K. Q. Wang, L. S. Xu, L. Wang, Z. G. Li, and Y. Z. Li, “Pulse baseline wander removal using wavelet approximation,” Computers in Cardiology 30, 605-608 (2005).

6. T. H. Fu, S. H. Liu, and K. T. Tang, “Heart rate extraction from photoplethysmogram waveform using wavelet multi-resolution analysis,” Journal of Medical and Biological Engineering 28(4), 229-232 (2008).

7. H. Han, M. J. Kim, and J. Kim, “Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography,” Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 1538-1541 (2007).

8. M. Aboy, J. McNames, and B. Goldstein, “Automatic detection algorithm of intracranial pressure waveform components,” Annual Reports of the Research Reactor Institute, Kyoto University 3, 2231-2234 (2001).

9. M. Aboy, C. Crespo, J. McNames, J. Bassale, and B. Goldstein, “Automatic detection algorithm for physiologic pressure signal components,” Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings 1, 196-197 (2002).

10. N. W. Townsend, and R. B. Germuska, Location features in a photopletysmograph signal, Patent US 2005/000479 A1, USA, A61B 5/02. 2005 (2005).

11. G. Strang, and T. Nguyen, Wavelets and Filters Banks, Wellesley-Cambridge-Press (1996). ISBN: 978-0961408879.

12. D. L. Donoho, “De-Noising by Soft-Thresholding,” IEEE Transactions on Information Theory 41(3), 613–627 (1995). Crossref

13. P. E. McSharry, and G. D. Clifford, “A realistic coupled nonlinear artificial ECG, BP and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms,” Proceedings of SPIE - The International Society for Optical Engineering 5467, 290-301 (2004).

14. A. A. Fedotov, “Amplitude–Time Method for Detecting Characteristic Pulse Wave Points,” Biomedical Engineering 46(6), 241-245 (2013). Crossref

15. R. M. Rangayyan, Biomedical signal analysis: a case-study approach, John Wiley & Sons, Inc., New York, (2002). ISBN: 978-0471208112.

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