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A new approach to complicated and noisy physiological waveforms analysis: peripheral venous pressure waveform as an example

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Abstract

We introduce a recently developed nonlinear-type time–frequency analysis tool, synchrosqueezing transform (SST), to quantify complicated and noisy physiological waveform that has time-varying amplitude and frequency. We apply it to analyze a peripheral venous pressure (PVP) signal recorded during a seven hours aortic valve replacement procedure. In addition to showing the captured dynamics, we also quantify how accurately we can estimate the instantaneous heart rate from the PVP signal.

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Notes

  1. When we are concerned with the statistical properties, the stationarity is defined to have a zero mean and the covariance between any two time points only depends on the time difference. We do not pursue this kind of stationarity in this work.

  2. The power spectrum is usually defined as the magnitude squared of the Fourier transform of a given signal. In this paper, to avoid the normalization issue, we call the magnitude of the Fourier transform of a given signal the power spectrum.

References

  1. Wardhan R, Shelley K. Peripheral venous pressure waveform. Curr Opin Anaesthesiol. 2009;22:814–21.

    Article  Google Scholar 

  2. Charalambous C, et al. Comparison of peripheral and central venous pressures in critically Ill patients. Anaesth Intensive Care. 2003;31:34–9.

    Article  CAS  Google Scholar 

  3. Weingarten T, Sprung J, Munis J. Peripheral venous pressure as a measure of venous compliance during pheochromocytoma resection. Anesth Analg. 2004;99:1035–7.

    Article  Google Scholar 

  4. Kim S, et al. Peripheral venous pressure as an alternative to central venous pressure in patients undergoing laparoscopic colorectal surgery. Br J Anaesth. 2011;106:305–11.

    Article  CAS  Google Scholar 

  5. Sperry BW, et al. Peripheral venous pressure measurements in patients with acute decompensated heart failure (PVP-HF). Circulation. 2017;10(7):e001430.

    Google Scholar 

  6. Sahin A, et al. Effect of catheter site on the agreement of peripheral and central venous pressure measurements in neurosurgical patients. J Clin Anesth. 2005;17:348–52.

    Article  Google Scholar 

  7. Anter A, Bondok R. Peripheral venous pressure is an alternative to central venous pressure in paediatric surgery patients. Acta Anaesthesiol Scand. 2004;48:1101–4.

    Article  CAS  Google Scholar 

  8. Bombardieri A, et al. Comparative utility of centrally versus peripherally transduced venous pressure monitoring in the perioperative period in spine surgery patients. J Clin Anesth. 2014;24:542–8.

    Article  Google Scholar 

  9. Amar D, et al. Correlation of peripheral venous pressure and central venous pressure in surgical patients. J Cardiothorac Vasc Anesth. 2001;15:40–3.

    Article  CAS  Google Scholar 

  10. Amoozgar H, et al. Correlation between peripheral and central venous pressures in children with congenital heart disease. Pediatr Cardiol. 2008;29:281–4.

    Article  CAS  Google Scholar 

  11. Milhoan K, et al. Upper extremity peripheral venous pressure measurements accurately reflect pulmonary artery pressures in patients with cavopulmonary or Fontan connections. Pediatr Cardiol. 2004;25:17–9.

    Article  CAS  Google Scholar 

  12. Baty L, Russo P, Tobias J. Measurement of central venous pressure from a peripheral intravenous catheter following cardiopulmonary bypass in infants and children with congenital heart disease. J Intensive Care Med. 2008;23:136–42.

    Article  Google Scholar 

  13. Hoftman N, et al. Peripheral venous pressure as a predictor of central venous pressure during orthotopic liver transplantation. J Clin Anesth. 2006;18:251–5.

    Article  Google Scholar 

  14. Hadimioglu N, et al. Correlation of peripheral venous pressure and central venous pressure in kidney recipients. Transplant Proc. 2006;38:440–2.

    Article  CAS  Google Scholar 

  15. Choi S, et al. Can peripheral venous pressure be an alternative to central venous pressure during right hepatectomy in living donors? Liver Transpl. 2007;13:1414–21.

    Article  Google Scholar 

  16. Daubechies I, Maes S. A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. In: Aldroubi A, Unser M, editors. Wavelets in Medicine and Biology. Boca Raton: CRC Press; 1996. p. 527–546.

    Google Scholar 

  17. Chen Y-C, Cheng M-Y, Wu H-T. Nonparametric and adaptive modeling of dynamic seasonality and trend with heteroscedastic and dependent errors. JRSS-B. 2014;76(3):651–82.

    Article  Google Scholar 

  18. Daubechies I, Lu J, Wu H-T. Synchrosqueezed Wavelet Transforms: An empirical mode decomposition-like tool. Appl Comp Harmon Anal. 2011;30(1):243–61.

    Article  Google Scholar 

  19. Flandrin P. Time-frequency/time-scale analysis. Wavelet analysis and its applications. Cambridge: Academic Press; 1998.

    Google Scholar 

  20. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart rate variability. standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93:1043–65.

    Article  Google Scholar 

  21. Shelley KH. Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate. Anesth Analg. 2007;105(6):S31–S3636.

    Article  Google Scholar 

  22. Nilsson L. Respiration signals from photoplethysmography. Anesth Analg. 2013;117(4):859–65.

    Article  Google Scholar 

  23. Alian AA, Shelley KH. Respiratory physiology and the impact of different modes of ventilation on the photoplethysmographic waveform. Sensors. 2012;12:2236–54.

    Article  Google Scholar 

  24. Lu S, et al. Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? J Clin Monit Comput. 2008;22(1):23–9.

    Article  Google Scholar 

  25. Daubechies I. Ten lectures on wavelets. Bangkok: SIAM; 1992.

    Book  Google Scholar 

  26. Alian A, et al. Impact of central hypovolemia on peripheral venous pressure waveform parameters in healthy volunteers. Physiol Meas. 2014;35(7):1509–20.

    Article  Google Scholar 

  27. Elgendi M. Detection of c, d, and e waves in the acceleration photoplethysmogram. Comput Methods Programs Biomed. 2014;117(2):125–36.

    Article  Google Scholar 

  28. Johnson A, et al. Multimodal heart beat detection using signal quality indices. Physiol Meas. 2015;36(8):1665–777.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics and Department of Statistical Science, Duke University, 140 Science Drive, Durham, NC, 27705, USA

    Hau-Tieng Wu

  2. Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan, ROC

    Hau-Tieng Wu

  3. Department of Anesthesiology, Yale University, 333 Cedar Street, P.O. Box 208051, New Haven, CT, 06520-8051, USA

    Aymen Alian & Kirk Shelley

Authors
  1. Hau-Tieng Wu
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  2. Aymen Alian
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  3. Kirk Shelley
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Contributions

HTW: idea, literature review, data analysis and write-up. AA: idea, literature review, data collection, and write-up. KS: idea, literature review, data collection, and write-up.

Corresponding authors

Correspondence to Hau-Tieng Wu or Kirk Shelley.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Yale-New Haven Institutional Review Board 1308012621), and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Wu, HT., Alian, A. & Shelley, K. A new approach to complicated and noisy physiological waveforms analysis: peripheral venous pressure waveform as an example. J Clin Monit Comput 35, 637–653 (2021). https://doi.org/10.1007/s10877-020-00524-9

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  • DOI: https://doi.org/10.1007/s10877-020-00524-9

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