Development of a method for differential analysis of data on the arterial blood oxygenation in healthy adults
DOI:
https://doi.org/10.15587/1729-4061.2021.244924Keywords:
arterial blood oxygenation, variability, differential analysis, Poisson and Erlang distributions, COVID-19Abstract
Monitoring of arterial blood saturation with oxygen (oxygenation) has gained special significance as a result of the COVID-19 pandemic. A new method for computer processing of saturation records (so-called SaO2 signals), based on the study of differentials (increments) from signals, was proposed. Finding a differential for a time series involves calculating the difference between the pairs of its adjacent elements. The differential is non-zero only if the elements in a pair are different. The study of differentials together with primary signals for a set of records (20 subjects) shows that the spectrum of observed levels of blood saturation is discrete and limited (from 2 to 10 levels). In addition, changes in saturation levels (switches) occur only between the nearest levels.
New indicators of the variability of blood saturation were proposed. These are the frequencies of saturation level switches (event intensities) and the intervals between them. It was established that these indicators are described by statistical distributions of Poisson and Erlang, respectively. Comparison of new variability indicators with the most reliable statistical – inter-quartile range – indicates that the new indicators also provide for the division of the data set into three subgroups according to the magnitude of variability. This division is statistically significant at a confidence level of 0.99 in both approaches, however, the division into sub-groups is slightly different in these methods.
It was shown that the proposed indicators of the variability of SaO2 signals are scale-invariant, that is, they do not depend on the length of observation interval. This is a consequence of the fractality of the positions of differentials in the observation interval. The established switch frequencies for subgroups in order of increasing variability are (0.06, 0.11, and 0.20) Hz. These frequencies are manifested on Fourier spectra of differentials of SaO2
References
Herrmann, J., Mori, V., Bates, J. H. T., Suki, B. (2020). Modeling lung perfusion abnormalities to explain early COVID-19 hypoxemia. Nature Communications, 11 (1). doi: http://doi.org/10.1038/s41467-020-18672-6
Kashani, K. B. (2020). Hypoxia in COVID-19: Sign of Severity or Cause for Poor Outcomes. Mayo Clinic Proceedings, 95 (6), 1094–1096. doi: http://doi.org/10.1016/j.mayocp.2020.04.021
Xie, J., Covassin, N., Fan, Z., Singh, P., Gao, W., Li, G. et. al. (2020). Association Between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clinic Proceedings, 95 (6), 1138–1147. doi: http://doi.org/10.1016/j.mayocp.2020.04.006
Hypoxemia (low blood oxygen) (2018). Mayo Clinic. Available at: https://www.mayoclinic.org/symptoms/hypoxemia/basics/definition/sym-20050930 Last accessed: 21.09.2021
Niknafs, P., Norouzi, E., Bahman, B. B., Baneshi, M. R. (2015). Can we Replace Arterial Blood Gas Analysis by Pulse Oximetry in Neonates with Respiratory Distress Syndrome who are Treated According to INSURE Protocol? Iranian Journal of Medical Sciences, 40 (3), 264–267. Available at: https://www.researchgate.net/publication/277080542_Can_we_Replace_Arterial_Blood_Gas_Analysis_by_Pulse_Oximetry_in_Neonates_with_Respiratory_Distress_Syndrome_who_are_Treated_According_to_INSURE_Protocol Last accessed: 21.09.2021
Jubran, A. (2015). Pulse oximetry. Critical Care, 19 (1). doi: http://doi.org/10.1186/s13054-015-0984-8
Mack, E. (2007). Focus on Diagnosis: Co-oximetry. Pediatrics in Review, 28 (2), 73–74. doi: http://doi.org/10.1542/pir.28-2-73
Chushkin, M., Popova, L., Shergina, E,, Krasnikova, E., Gordeeva, O., Karpina, N. (2020). Comparative analysis of the arterial oxygen saturation (SaO2) and pulse oximetry measurements (SpO2) in patients with pulmonary tuberculosis. European Respiratory Journal, 56. doi: http://doi.org/10.1183/13993003.congress-2020.3209
Wilson-Baig, N., McDonnell, T., Bentley, A. (2021). Discrepancy between SpO2 and SaO2 in patients with COVID‐19. Anaesthesia, 76 (S3), 6–7. doi: http://doi.org/10.1111/anae.15228
Shenoy, N., Luchtel, R., Gulani, P. (2020). Considerations for target oxygen saturation in COVID-19 patients: are we under-shooting? BMC Medicine, 18 (1). doi: http://doi.org/10.1186/s12916-020-01735-2
Fossion, R., Fossion, J. P. J., Rivera, A. L., Lecona, O. A., Toledo-Roy, J. C., García-Pelagio, K. P. et. al.; Olivares-Quiroz, L., Resendis-Antonio, O. (Eds.) (2018). Homeostasis from a Time-Series Perspective: An Intuitive Interpretation of the Variability of Physiological Variables. Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Cham: Springer, 87–109. doi: http://doi.org/10.1007/978-3-319-73975-5_5
Chuiko, G., Dvornik, O., Darnapuk, Y., Baganov, Y. (2021). Devising a new filtration method and proof of self-similarity of electromyograms. Eastern-European Journal of Enterprise Technologies, 4 (9 (112)), 15–22. doi: http://doi.org/10.15587/1729-4061.2021.239165
Yoshida, M., Onda, K., Wada, Y., Kuwahara, M. (2015). Influence of sickness condition on diurnal rhythms of heart rate and heart rate variability in cows. Journal of Veterinary Medical Science, 77 (3), 375–379. doi: http://doi.org/10.1292/jvms.14-0402
Bhogal, A. S., Mani, A. R. (2017). Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation. Frontiers in Physiology, 8. doi: http://doi.org/10.3389/fphys.2017.00555
Frost, J. (2019). Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries. Available at: https://www.goodreads.com/book/show/53632396-introduction-to-statistics Last accessed: 21.09.2021
Chuiko, G., Darnapuk, Y., Dvornik, O., Kraynik, Y., Yaremchuk, O., Haab, R. (2021). A New Way of Data Analysis and Rating of the Blood Oxygen Saturation Variability. 2021 IEEE 12th International Conference on Electronics and Information Technologies (ELIT), 51–54. doi: http://doi.org/10.1109/elit53502.2021.9501109
Chuiko, G., Darnapuk, Y., Dvornik, O., Kraynik, Y., Yaremchuk, O., Davidenko, A. (2021). "Devil`s stairs", Poisson`s Statistics, and Patient Sorting via Variabilities for Oxygenation: All from Arterial Blood Gas Data. doi: http://doi.org/10.1101/2021.08.10.455835
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G. et. al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.. Circulation, 101 (23), e215–e220. doi: http://doi.org/10.1161/01.cir.101.23.e215
Ghassemi, M., Moody, B., Lehman, L., Song, C., Li, Q., Sun, H. et. al. (2018). You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018. 2018 Computing in Cardiology Conference (CinC). doi: http://doi.org/10.22489/cinc.2018.049
Bernardin, L., Chin, P., DeMarco, P., Geddes, K. O., Hare, D. E. G., Heal, K. M. et. al. (2020). Maple Programming Guide. Maplesoft, a division of Waterloo Maple Inc. Available at: https://maplesoft.com/documentation_center/maple2020/ProgrammingGuide.pdf Last accessed: 21.09.2021
Karlis, D., Xekalaki, E. (2007). Mixed Poisson Distributions. International Statistical Review, 73 (1), 35–58. doi: http://doi.org/10.1111/j.1751-5823.2005.tb00250.x
Scott, D. W. (2010). Averaged shifted histogram. Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2), 160–164. doi: http://doi.org/10.1002/wics.54
Weglarczyk, S. (2018). Kernel density estimation and its application. ITM Web of Conferences, XLVIII Seminar of Applied Mathematics, 23 (2). doi: http://doi.org/10.1051/itmconf/20182300037
Tetrax: Fourier transformation of postural sway, Sunlight. Available at: http://postureetmesure.free.fr/docs/TetraxFourierTransformation.pdf Last accessed: 21.09.2021
Amaral, L. (2012). A Brief Overview of Multifractal Time Series. Available at: https://archive.physionet.org/tutorials/multifractal/ Last accessed: 21.09.2021
Banerjee, S., Easwaramoorthy, D., Gowrisankar, A. (2021). Fractal Functions, Dimensions and Signal Analysis. Cham: Springer. doi: http://doi.org/10.1007/978-3-030-62672-3
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Gennady Chuiko, Yevhen Darnapuk
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
