UDC 51-7:537.86

Time frequency analysis of the blood pressure signals using the Hilbert-Huang transform

 

V. D. Ompokov, V. V. Boronoyev

Institute of Physical Materials Science of Sberian Branch of Russian Academy of Sciences, Sakhyanovoy 6, Ulan-Ude 670047, Russia

 

The paper is received on August 1, 2019

 

Abstract. The paper presents a relative new method for the analysis of nonstationary signals that allows a signal's frequency and amplitude to be evaluated with high time resolution. The Hilbert-Huang transform consists of two steps: Empirical mode decomposition and Hilbert transform. The empirical mode decomposition is a signal analysis method that separates multi-component signals into single oscillatory modes called intrinsic mode functions. Mode mixing problem happens during the empirical mode decomposition process. Fourier transform was introduced to remove the mode-mixing effect. Numerical experiments with signals containing closely spaced spectral components are carried out. Mean square error between predefined and decomposed data was used as estimation parameter. The proposed approach allows achieving better decomposition results than the classic empirical mode decomposition. The results of the using of Hilbert-Huang transform as an instrument for digital processing biomedical signals are presented.

Key words: time-frequency analysis, Hilbert-Huang transform, empirical mode decomposition, mode mixing, Fourier transform, arterial blood pressure signal.

References

1.       Norden Huang et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. 1998.. A 454. p. 903–995.

2.       N. E. Huang, Samuel S.P. Shen. The Hilbert-Huang transform and its applications. World Scientific Publishing Co. Pte. Ltd. 2005. 311 p.

3.       Filatova A.E., Pavlov A.N., Hramov A.E. Time-frequency analysis of nonstationary geophysical signals on the basis of wavelets and empirical modes. Vestnik TGU [Tambov University Reports]. 2012. Vol. 17. No. 5. p. 1428-1432. (In Russian)

4.       Safiullin N.T. Razrabotka metodiki analiza vremennykh ryadov s pomoshch'yu preobrazovaniya Khuanga-Gil'berta [Development of a time series analysis technique using the Hilbert-Huang transform]. PhD in Engineering sciences dissertation. Novosibirsk. 2015. 193 p. (In Russian).

5.       Davydov V.A., Davydov A.V. Cleaning geophysical data from noise using the Hilbert–Huang transform. Online scientifiv resource «Aktualnye innovatsionnye issledovaniya: nauka i praktika» [Actual innovative research: science and practice]. 2010. No. 1. Available at: http://www.actualresearch.ru. (In Russian).

6.       Krivonogov L.Yu. Analysis and processing of empirical modes to suppress interference in electrocardiograms. Izvestiya yuzhnogo federal'nogo universiteta. Tekhnicheskiye nauki  [Bulletin of Sourth federal University. Technical Sciences]. 2012. Vol. 134. No. 9. p. 119-125.

7.       Boronoev V.V., Ompokov V.D.. The Hilbert-Huang Transform for biomedical signals processing. 2014 International conference on computer technologies in physical and engineering applications (ICCTPEA). Saint Petersburg State University; IEEE (IEEE Catalog number CFP14BDA-USB), 2014, p. 21-22.

8.       Boronoyev V.V., Ompokov V.D. The Hilbert-Huang Transform for Biomedical Signal Analysis. Biomedicinskaya radioehlektronika [Biomedical Radioelectronics]. 2014. No.3. P.40-44. (In Russian).

9.       Boronoev V. V., Ompokov V. D., Pupysheva N. V., Garmaev B. Z.. The estimation of optical radiation influence on human pulse waves. Proceedings of SPIE. 24th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 2018, Vol. 10833, DOI: 10.1117/12.2501927.

10.  Ompokov V.D., Boronoyev V.V. Hilbert-Huang Transform for Blood pressure signals time-frequency analysis. Zhurnal Radioelektroniki [Journal of Radio Electronics]. 2017. No.5. Available at http://jre.cplire.ru/jre/may17/8/text.pdf (In Russian).

11.  Altay Y.A., Kremlev A. S. Comparative analysis of ECG signal processing methods in the time-frequency domain. Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2018, P. 1058-1062.

12.  Altay Y.A., Kremlev A.S. Analysis and systematization of noise arising by long-term recording of ECG signal. Proceedings of the IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, 2018, P.1053-1057.

13.  Zhaohua Wu, Norden E. Huang. Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 2009, Vol. 1, No. 1, P. 1–41.

14.  María E. Torres, Marcelo A. Colominas; Gastón Schlotthauer, Patrick Flandrin. A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011, P. 4144-4147.

15.  Marcelo A. Colominas; Gastón Schlotthauer, María E. Torres. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 2014, vol. 14, P. 19-29.

16.  Ompokov V.D., Boronoyev V.V. CEEMDAN as a Method of Solving the Main Problems of HHT Applying. Zhurnal Radioelektroniki [Journal of Radio Electronics]. 2016. No. 9. Available at http://jre.cplire.ru/jre/sep16/12/text.pdf  (In Russian).

17.  Altay Y.A., Kremlev A. S. Improved Noise Resistance Processing Electrocardiosignals Adaptive Noise CEEMDAN as a Method of Solving the Basic Problems of the Application of Hilbert-Huang Transform. Proceedings of the 2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2017 ElConRus), 2017, P. 621-623.

 

For citation:

V. D. Ompokov, V. V. Boronoyev. Time frequency analysis of the blood pressure signals using the Hilbert-Huang transform.  Zhurnal Radioelektroniki - Journal of Radio Electronics. 2019. No. 8. Available at http://jre.cplire.ru/jre/aug19/4/text.pdf

DOI  10.30898/1684-1719.2019.8.4