Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)


For citation:

Choi M. O., Postnov D. E. Method for determining significant components for assessing pulse wave shape variability. Izvestiya of Sarat. Univ. Physics. , 2021, vol. 21, iss. 1, pp. 36-47. DOI: 10.18500/1817-3020-2021-21-1-36-47

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.03.2021
Full text:
(downloads: 21)
Language: 
Russian
Article type: 
Article
UDC: 
577.31:577.35:53.047

Method for determining significant components for assessing pulse wave shape variability

Autors: 
Choi Marya Olegovna, Saratov State University
Postnov Dmitry Engelevich, Saratov State University
Abstract: 

Background and Objectives: The conventional approach to the quantification of the pulse wave is based on the assessment of the features of its shape within each beat to beat heart interval. Usually, a set of indices is calculated (such as heart efficiency index, reflection index, stiffness index), which are determined by the reference points of the wave contour. We have developed an alternative method aimed to analyze the variability of the pulse waveform regardless of the variability of its rhythm. A distinctive feature of the method is that the classical spectral analysis tool – the Fourier series expansion in harmonic functions – is used not for frequency analysis, but for describing the features of the pulse waveform, regardless of its frequency-time characteristics. Materials and Methods: The data is represented by the signal as separate sets of fragments, each of which corresponds to one cardiointerval. Further, information about the current heart rate, including its variability, is removed from the data. We do this by resampling each cardiointerval by the same predetermined number of samples. The shape variability of each waveform is quantified by calculating the amplitudes and phases of the harmonics of each waveform as a representative of a strictly periodic sequence. This is an important point, since the original pulse wave signal belongs to the class of random signals and for it, as for the whole, the amplitude and phase of the Fourier spectra are not determined. As a result of the procedure, the analysis of the pulse waveform for each cardiointerval is reduced to the analysis of the amplitude and phases of the required number of harmonics. The method described above was used to process the data of an experiment aimed at quantitatively calculating the relationship between the central and distal pulses. The measurements were carried out on a group of 16 healthy volunteers aged 20–35 years after being in a calm state for 20 minutes. Then, rheographic signals were synchronously recorded from three points on the human skin surface (aortic region, wrist and distal phalanx of the finger). Results: The study revealed significant differences in the stability of the shape of the pulse waves recorded in different parts of the vascular bed, which is expressed in different degrees of variability of their main components. It is important that the central pulse has a smaller number of significant harmonics in comparison with the distal one, and is more stable at the first 4 harmonics containing the main signal power. Conclusion: We proposed and tested a method for analyzing the variability of the pulse waveform, based on the harmonic analysis of the re-sampled signal for each of the cardiointervals, aimed at studying the variability of the pulse waveform separately from the variability of its rhythm. The obtained quantitative data on the stability of the harmonics of the central pulse wave indicate the prospects for further development of the transfer function method in the problem of restoring the shape of the central pulse based on distal measurements.

Acknowledgments: 
The authors are grateful to Professor Victor A. Klochkov (Saratov State Medical University named after V. I. Razumovsky) for the help in organizing experiments and useful discussions.
Reference: 
  1. Rangayyan R. M. Analiz biomedicinskih signalov. Prakticheskij podhod [Biomedical Signal Analysis. A CaseStudy Approach]. Moscow, FIZMATLIT Publ., 2010. 440 p. (in Russian).
  2. Millasseau S. C., Ritter J. M., Takazawa K., Chowienczyk P. J. Contour analysis of the photoplethysmographic pulse measured at the finger. Journal of Hypertension, 2006, vol. 24, no. 8, pp. 1449–1456. DOI:  https://doi.org/10.1097/01.hjh.0000239277.05068.87
  3. Korpas D., Halek J., Doleˇzal L. Parameters describing the pulse wave. Physiological Research, 2009, vol. 58, no. 8, pp. 473–482.
  4. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 2007, vol. 28, no. 3, R1-39. DOI:  https://doi.org/10.1088/0967-3334/28/3/R01
  5. Lapitan D. G., Glazkov A. A., Rogatkin D. A. Evaluation of the age-related changes of elasticity of peripheral vascular walls by photoplethysmography. Medicinskaya fizika [Medical Physics], 2020, vol. 3, no. 87, pp. 71–77 (in Russian).
  6. Takazawa K., Tanaka N., Fujita M., Matsuoka O., Saiki T., Aikawa M., Tamura S., Ibukiyama C. Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension, 1998, vol. 32, no. 2, pp. 365–370. DOI:  https://doi.org/10.1161/01.HYP.32.2.365
  7. Imanaga I., Hara H., Koyanagi S., Tanaka K. Correlation between wave components of the second derivative of plethysmogram and arterial distensibility. Japanese Heart Journal, 1998, vol. 39, no. 6, pp. 775–784. DOI:  https://doi.org/10.1536/ihj.39.775
  8. Baatyrov R. T., Kalinkin M. Yu., Usanov A. D., Dobdin S. Yu., Skripal An. V. Estimation of the Value of Reverse Blood Flow in the Artery by the Second Derivative of the Pulse Pressure Wave.Izvestiya of Saratov University. New Series. Series: Physics, 2020, vol. 20, iss. 3, pp. 178–182 (in Russian). DOI: https://doi.org/10.18500/1817-3020-2020-20-3-178-182
  9. Sokolova I. V., Yarullin X. X. Informative value of the two-component rheogram analysis method. Klinicheskaya Medicina [Clinical Medicine], 1983, no. 7, pp. 94–101 (in Russian).
  10. Marcinkevics Z., Kusnere S., Aivars J. I., Rubins U., Zehtabi A. H. The shape and dimensions of photoplethysmographic pulse waves: A measurement repeatability study. Acta Universitatics Latviensis Biology, 2009, vol. 753, pp. 99–106.
  11. Mamontov O. V., Shcherbinin A. V., Romashko R. V., Kamshilin A. A. Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light. Applied Sciences, 2020, vol. 10, no. 18, pp. 6192. DOI:  https://doi.org/10.3390/app10186192
  12. Guchuk V. V. Composite algorithm for separation of the periods of a pulse signal in medical diagnostics tasks. Tenth International Conference Management of LargeScale System Development (MLSD), 2017, pp. 1–4. DOI:  https://doi.org/10.1109/MLSD.2017.8109635
  13. Desova A., Guchuk V., Pokrovskaya I., Dorofeyuk A. Intelligent Analysis of Quasiperiodic Bioosignals in Medical Diagnostic Problems (with the Example of a Pulse Signal). Avtomatika i Telemekhanika [Automation and Telemechanics], 2018, no. 11, pp. 3–15 (in Russian). DOI:  https://doi.org/10.31857/S000523100002773-4
  14. Desova A. A., Guchuk V. V., Dorofeyuk A. A. A new approach to pulse signal rhythmic structure analysis. International Journal of Biomedical Engineering and Technology, 2014, vol. 14, no. 2, pp. 148–158. DOI:  https://doi.org/10.1504/IJBET.2014.059344
  15. Nishijima T., Nakayama Y., Tsumura K., Yamashita N., Yoshimaru K., Ueda H., Yoshikawa J. Pulsatility of ascending aortic blood pressure waveform is associated with an increased risk of coronary heart disease. American Journal of Hypertension, 2001, vol. 14, no. 5, pp. 469–473. DOI:  https://doi.org/10.1016/S0895-7061(00)01288-7
  16. Safar M. E., Blacher J., Pannier B., Guerin A. P., Marchais S. J., Guyonvarc’h P. M., London G. M. Central pulse pressure and mortality in end-stage renal disease. Hypertension, 2002, vol. 39, no. 3, pp. 735–738. DOI:  https://doi.org/10.1161/hy0202.098325
  17. Roman M. J., Devereux R. B., Kizer J. R., Lee E. T., Galloway J. M., Ali T., Howard B. V. Central pressure more strongly relates to vascular disease and outcome than does brachial pressure: The Strong Heart Study. Hypertension, 2007, vol. 50, no. 1, pp. 197–203. DOI:  https://doi.org/10.1161/hy0202.098325
  18. Kobalava Z. D., Kotovskaya Yu. V., Akhmetov R. E. Arterial rigidity and central pressure: novel aspects of pathophysiology and therapy. Arterial’naya Gipertenziya [Arterial Hypertension], 2010, vol. 16, no. 2, pp. 126–133 (in Russian).
  19. Miyashita H. Clinical assessment of central blood pressure. Current Hypertension Reviews, 2012, vol. 8, no. 2, pp. 80–90. DOI:  https://doi.org/10.2174/157340212800840708
  20. Agnoletti D., Zhang Y., Salvi P., Borghi C., Topouchian J., Safar M. E., Blacher J. Pulse pressure amplification, pressure waveform calibration and clinical applications. Atherosclerosis, 2012, vol. 224, no. 1, pp. 108–112. DOI:  https://doi.org/10.1016/j.atherosclerosis.2012.06.055
  21. Chen C. H., Nevo E., Fetics B., Pak P. H., Yin F. C., Maughan W. L., Kass D. A. Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure: Validation of generalized transfer function. Circulation, 1997, vol. 95, no. 7, pp. 1827–1836. DOI:  https://doi.org/10.1161/01.CIR.95.7.1827
  22. Hope S. A., Meredith I. T., Cameron J. D. Arterial transfer functions and the reconstruction of central aortic waveforms: Myths, controversies and misconceptions. Journal of Hypertension, 2008, vol. 26, no. 1, pp. 4–7. DOI:  https://doi.org/10.1097/HJH.0b013e3282f0c9f5
  23. Kuznecov N. A., Sinicyn I. N. Development of the Kotelnikov counting theorem. Physics Uspekhi, 2009, vol. 179, no. 2, pp. 216–218 (in Russian). DOI:  https://doi.org/10.3367/UFNr.0179.200902j.0216
Received: 
31.10.2020
Accepted: 
21.01.2021
Published: 
31.03.2021