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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 9468503, 8 pages
https://doi.org/10.1155/2017/9468503
Research Article

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

1College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China
2The First Hospital of Jilin University, Changchun 130021, China

Correspondence should be addressed to Shu Diao; moc.qq@989996271

Received 28 March 2017; Revised 28 July 2017; Accepted 7 August 2017; Published 7 November 2017

Academic Editor: Thierry Busso

Copyright © 2017 Suyi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Tamura, Y. Maeda, M. Sekine, and M. Yoshida, “Wearable photoplethysmographic sensors—past and present,” Electronics, vol. 3, no. 2, pp. 282–302, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. P. S. Addison, J. N. Watson, M. L. Mestek, J. P. Ochs, A. A. Uribe, and S. D. Bergese, “Pulse oximetry-derived respiratory rate in general care floor patients,” Journal of Clinical Monitoring and Computing, vol. 29, no. 1, pp. 113–120, 2015. View at Google Scholar
  3. S. Lu, H. Zhao, K. Ju et al., “Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information?” Journal of Clinical Monitoring and Computing, vol. 22, no. 1, pp. 23–29, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Schäfer and J. Vagedes, “How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram,” International Journal of Cardiology, vol. 166, no. 1, pp. 15–29, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. R. W. C. G. R. Wijshoff, M. Mischi, and R. M. Aarts, “Reduction of periodic motion artifacts in photoplethysmography,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 196–207, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Lee, J. Han, H. J. Baek, J. H. Shin, K. S. Park, and W. J. Yi, “Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry,” Physiological Measurement, vol. 31, no. 12, pp. 1585–1603, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. M.-Z. Poh, N. C. Swenson, and R. W. Picard, “Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 3, pp. 786–794, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Han and J. Kim, “Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method,” Computers in Biology and Medicine, vol. 42, no. 4, pp. 387–393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. M. R. Ram, K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy, “A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 5, pp. 1445–1457, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Yousefi, M. Nourani, S. Ostadabbas, and I. Panahi, “A motion-tolerant adaptive algorithm for wearable photoplethysmographic biosensors,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, pp. 670–681, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. L. A. Bortolotto, J. Blacher, T. Kondo, K. Takazawa, and M. E. Safar, “Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity,” American Journal of Hypertension, vol. 13, no. 2, pp. 165–171, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Reşit Kavsaoǧlu, K. Polat, and M. Recep Bozkurt, “A novel feature ranking algorithm for biometric recognition with PPG signals,” Computers in Biology and Medicine, vol. 49, no. 1, pp. 1–14, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Galeotti, C. G. Scully, J. Vicente, L. Johannesen, and D. G. Strauss, “Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting,” Physiological Measurement, vol. 36, no. 8, article no. 1705, pp. 1705–1716, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. 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, vol. 28, no. 4, pp. 229–232, 2008. View at Google Scholar · View at Scopus
  15. H. S. Shin, C. Lee, and M. Lee, “Adaptive threshold method for the peak detection of photoplethysmographic waveform,” Computers in Biology and Medicine, vol. 39, no. 12, pp. 1145–1152, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. S.-H. Liu, K.-M. Chang, and T.-H. Fu, “Heart rate extraction from photoplethysmogram on fuzzy logic discriminator,” Engineering Applications of Artificial Intelligence, vol. 23, no. 6, pp. 968–977, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Sun, P. Yang, Y. Li et al., “Robust heart beat detection from photoplethysmography interlaced with motion artifacts based on empirical mode decomposition,” in Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 775–778, IEEE, Hong Kong and Shenzhen, China, January 2012.
  18. A. R. Kavsaoqlu, K. Polat, and M. R. Bozkurt, “An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, no. 3, pp. 1792–1796, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Zhao and B. Ye, “Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis,” Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1062–1075, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. I. Daubechies, “Ten lectures on wavelets: CBMS-NSF regional conference series in applied mathematics,” Society for Industrial and Applied Mathematics, pp. 129–166, 1992. View at Google Scholar