• 1990 (Vol.4)
  • 1989 (Vol.3)
  • 1988 (Vol.2)
  • 1987 (Vol.1)

METHODOLOGICAL RECOMMENDATIONS FOR THE CREATION OF SENSOR MEASUREMENT SYSTEMS FOR RESPIRATORY RATE MONITORING BASED ON PHOTOPLETHYSMOGRAPHIC SIGNAL PROCESSING

© 2024 P. B. Petrenko

Synergy Design Bureau, Signal Processing Center 108, Ligovsky Avenue, St. Petersburg, Russia

Received 27 Mar 2024

A methodical apparatus for creating sensor measurement systems for monitoring human respiration rate is proposed. It includes a method for estimating respiratory rate based on statistical analysis of photoplethysmographic signals (human pulse wave), a method for selecting priority regions for estimating respiratory rate, and a criterion for determining the required bracelet tension during measurements. The application of the respiratory rate estimation method involves calculating the Correntropy spectral density of the pulse wave signal. A distinctive feature of the method is the use of an algorithm for selecting the priority empirical mode of the Hilbert-Huang decomposition, which is most closely related to the respiratory rate. Experimental verification of the method showed that the mean value of the absolute error for 58.8% of the sample of calculated respiratory rate values did not exceed 1 breath/min, and the 95% confidence interval for the mean absolute error of the entire sample was [0.72–2.2] breaths/min.

Key words: photoplethysmogram pulse wave, respiratory motion frequency, optimal kernel Correntropy function, empirical Hilbert- Huang distribution, instantaneous Hilbert frequency, discrete Fourier transform

DOI: 10.31857/S0235009224030057  EDN: BRXSED

Cite: Petrenko P. B. Metodicheskie rekomendatsii po sozdaniyu sensornykh izmeritelnykh sistem monitoringa chastoty dykhaniya na osnove obrabotki fotopletizmograficheskikh signalov [Methodological recommendations for the creation of sensor measurement systems for respiratory rate monitoring based on photoplethysmographic signal processing]. Sensornye sistemy [Sensory systems]. 2024. V. 38(3). P. 82–94 (in Russian). doi: 10.31857/S0235009224030057

References:

  • Aificher E. S., Dzhervis B. U. Tsifrovaya obrabotka signalov: prakticheskii podkhod: per. s angl. [Digital Signal Processing: A Practical Approach]. Moscow. Williams Publishers, 2008. 992 p. (In Russian).
  • Garanin A. A., Shipunov I. D., Rubanenko A. O., Sannikova N. O. Beskontaktnye metody izmereniya chastoty dykhaniya: (obzor literatury). Vestnik novykh meditsinskikh tekhnologii. [Non-contact methods of respiratory rate measurement: (literature review). Bulletin of new medical technologies]. Electronic edition. 2023. № 5. P. 64–72. http://doi.org/ 10.24412/2075-4094-2023-5-1-9 (In Russian).
  • Gutsol L. O., Nepomnyashchikh S. F., Korytov L. I., Gubina M. I., Tsybikov N. N., Vitkovskii Yu.A. Fiziologicheskie i patofiziologicheskie aspekty vneshnego dykhaniya. [Physiologic and pathophysiologic aspects of external respiration]. State Budgetary Educational Institution of Higher Professional Education of State Medical University of Russia, Department of Pathologic Physiology with a Course of Clinical Immunology, Department of Normal Physiology. Irkutsk, IGMU, 2014. 116 p. (In Russian).
  • Kan S. C., Mikulovich A. V., Mikulovich V. I. Analiz nestatsionarnykh signalov na osnove preobrazovaniya Gil’berta-Khuanga [Analysis of non-stationary signals on the basis of Hilbert-Huang transform. Informatics]. Informatics [Informatika]. 2010. № 2. P. 25–35. (In Russian).
  • Kublanov V. S., Dolganov A. Yu., Kostousov V. B., Nemirko A. P. , Manilo L. A., Petrenko T. S., Gamboa H., Rodriges J. Biomeditsinskie signaly i izobrazheniya v tsifrovom zdravookhranenii: khranenie, obrabotka i analiz. [Biomedical signals and images in digital health care: storage, processing and analysis: textbook]. Yekaterinburg. Publ. of the Ural Univ. 2020. 240 p. (In Russian).
  • Marple Jr. S. L. Digital spectral analysis and its applications. Moscow. Mir Pabl. 1980. 584 p. (In Russian).
  • Rangaian R. M. Biomedical Signal Analysis. A CaseStudy Approach. Edited by A.P. Nemirko. Moscow. FIZMATLIT, 2007. 44 p. (In Russian).
  • Chang H-H. Hsu C. C., Chen C-Y., Lee W-K., Hsu H-T.,. Shyu K-K, Yeh J-R., Lin P.-J., Lee P-L. A Method for Respiration Rate Detection in Wrist PPG Signal Using Holo-Hilbert Spectrum. IEEE Sensors Journal. 2018. V.18(18), September 15. P. 11. http://doi.org/10.1109/JSEN.2018.2855974
  • Dehkordi P., Garde A., , Molavi B., Ansermino J. M., Dumont G. A. Extracting Instantaneous Respiratory Rate from Multiple Photoplethysmogram RespiratoryInduced Variations. Front. in Physiol. 2018. V. 9. P. 10. http://doi.org /10.3389/fphys.2018.00948
  • Elgendi M., Galli V., Ahmadizadeh C., Menon C. Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device. Data Descriptor. 2022. V. 7(9). № 132. P. 12. https://doi.org/10.3390/data7090132
  • Garde A., Karlen W., Ansermino J. M., Dumont G. A. Estimating Respiratory and Heart Rates from the Correntropy Spectral Density of the Photoplethysmogram. PLOS ONE. 2014. V. 9(1). P. 11. https://doi.org/10.1371/journal.pone.0086427
  • Herawati N. E., Nisa K., Setiawan E. The Optimal Bandwidth for Kernel Density Estimation of Skewed. Distributional: A Case Study on Survival Time Data of Cancer Patients. Presiding Seminar Nasional Metode Quantitative. 2017. P. 380–388.
  • Huang N. E., Hu K., Yang A. C., Chang H.-C., Jia D., Liang W.-K., Yeh J. R., Kao C.-L., Juan C.-H., Peng C.K., Meijer J. H., Wang Y.-H., Long S. R., Wu Z. On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data. Philosophical Transactions Series A. Mathematical, physical, and engineering sciences. 374 (2065): 201502062016. 2016. P. 21. http://dx.doi.org/10.1098/rsta.2015.0206
  • Huang N. E., Shen Z., Long S. R., . Wu M.L.C. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Ro. Soc. Lond. A.1998. V. 454. P. 903–995. http://dx.doi.org/10.1098/rspa.1998.0193
  • Huang N. E., Wu M-C., Long S. R., Shen S. S.P. , Qu W., Gloersen P., Fan K. L. A confidence limit for empirical mode decomposition and Hilbert spectral analysis. Proc. R. Soс.: Mathematical, Physical and Engineering Sciences. 2003. V. 459. P. 2317–23425. http://dx.doi.org/10.1098/rspa.2003.1123
  • Huang N. E., Wu Z., Long S. R., Arnold K. C., Chen X., Blank K. On instantaneous frequency. Advances in Adaptive Data Analysis. 2009. V. 1(2). P. 177–229. http://dx.doi.org/10.1142/S1793536909000096
  • Huang N. E , Wu. Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics. 2008. V. 46(2): RG2008. P. 23. http://dx.doi.org/10.1029/2007RG000228
  • Johansson A. Neural network for photoplethysmographic respiratory rate monitoring. Med. Biol. Eng. Computing. 2003. V. 41(3). P. 242–248. http://dx.doi.org/10.1007/BF02348427
  • Lázaro J., Gil E., Bailо`n R., Laguna P. Deriving Respiration from the pulse photoplethysmographic signal. Computing in Cardiology. 2011. V. 38. P. 713–716. https://www.researchgate.net/publication/254019768
  • Nita G. M., Gary D. E., Liu Z., Hurford G. J., White S. M. Radio Frequency Interference Excision Using SpectralDomain Statistics. The Astronomical Society of the Pacific. 2007. V. 119. P. 805–827. http://dx.doi.org/10.1086/520938
  • PPG-BP Database. 2022. https://figshare.com/articles/dataset/PPG-BP_Database_zip/5459299?file=9441097
  • Real-World PPG dataset. 2019. https://data.mendeley.com/datasets/yynb8t9x3d/1
  • Santamaria I., Pokharel P. P., Principe J. C. Generalized correlation function: definition, properties, and application to blind equalization. IEEE Transactions on Signal Processing. 2006, V. 54(6). P. 2187–2197. http://dx.doi.org/10.1109/TSP.2006.872524
  • Shelley K. H., Awad A. A., Stout R. G., Silverman D. G. https://pubmed.ncbi.nlm.nih.gov/?term=Silverman+DG&cau thor_id=16779621 The use of joint time frequency analysis to quantify the effect of ventilation on the pulse oximeter waveform. J. Clin. Monit. Compute. 2006. № 20(2). P. 81–87. http://dx.doi.org/10.1007/s10877-006-9010-7
  • Silverman B. W. Density Estimation for Statistics and Data Analysis. London. Chapman & Hall/CRC. 1998. P. 176. https://doi.org/10.1201/9781315140919
  • Tiara Medical. Kernel KN-601M. 2013. http://www.kernelmedical.ru/monitor/kn-601m
  • Vrabie V. D., Granjon P., Serviere C. Spectral Kurtosis: from Definition to Application. 6th IEEE International Workshop on Nonlinear Signal and Image Processing (NSIP 2003). 2003. P. 5. Grado-Trieste, Italy. hal-00021302. http//Hal. Science/ hal-00021302.
  • Weifeng L., Pokharel P. P., Principe J. C. Correntropy: Properties and Applications in Non-Gaussian Signal Processing. IEEE Transactions on Signal Processing. 2007. V. 55(11). P. 5286–5298. https://doi.org/10.1109/TSP.2007.896065