Analysis of photoplethysmographic signals using wavelet transform coefficients and scalogram for assessment of cardiovascular processes
Abstract
Remote photoplethysmography is a non-invasive method of measuring changes in blood volume, which
is widely used to assess the state of the cardiovascular system. However, due to the complexity of RPPG
signals and the low quality of the video stream, the analysis of these signals requires accurate and
adaptive data processing methods. Wavelet transform and scalogram are effective tools for detecting the
time-frequency characteristics of these signals. The aim of the study is to apply the wavelet transform
coefficients and scalogram for detailed analysis of photoplethysmographic signals in order to improve
the assessment of cardiovascular processes. In this work, wavelet transforms were used to obtain
coefficients that reflect the multiscale structure of RPPG signals. The analysis of the scalogram allows
us to identify key frequency and time changes that characterize cardiovascular processes. Particular
attention is paid to identifying the characteristic features of pulse oscillations and assessing their
interdependence with the state of the cardiovascular system.
References
Nakonechnyi A., Berezhnyi I., (2023) "Estimation of heart rate and its variability based on wavelet analysis of photoplethysmographic signals in real time", Intelligent data acquisition and advanced computing systems: technology and applications: proceedings of the 12th IEEE International conference IDAACS, Dortmund, Germany, 7-9 September 2023. Vol. 1. - 2023. - C. 765-770. ISSN: 27704262, ISBN: 979-835035805-6, DOI: 10.1109/IDAACS58523.2023.10348785
Berezhnyi I., Nakonechnyi A., (2024). "Analysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering", Advances in Cyber-physical Systems 2024, Volume 9, Number 1, pp. 82-88, ISSN: 2524-0382, DOI: 10.23939/acps2024.01.082G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
Wang, Xin, Tapani Ahonen, and Jari Nurmi. "Applying CDMA technique to network-on-chip." IEEE transactions on very large scale integration (VLSI) systems 15.10 (2007): 1091-1100. https://doi.org/10.1109/TVLSI.2007.903914
Arslanova, I., Galvez-Pol, A., Kilner, J. et al. Seeing Through Each Other's Hearts: Inferring Others' Heart Rate as a Function of Own Heart Rate Perception and Perceived Social Intelligence. Affec Sci 3, pp. 862-877 (2022) doi:10.1007/s42761-022-00151-4
L. Wang, H. Li, H. Qiu, Q. Wu, F. Meng and K. N. Ngan, "POS-Trends Dynamic-Aware Model for Video Caption," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 7, pp. 4751-4764, July 2022, doi: 10.1109/TCSVT.2021.3131721
A. Gudi, M. Bittner, R. Lochmans and J. van Gemert, "Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 1570-1579, doi: 10.1109/ICCVW.2019.00196.
Wang W, den Brinker AC, Stuijk S, de Haan G. Algorithmic Principles of Remote PPG. IEEE Trans Biomed Eng. 2017 Jul;64(7), pp. 1479-1491. doi: 10.1109/TBME.2016.2609282 https://doi.org/10.1109/TBME.2016.2609282
Serge Bobbia, Richard Macwan, Yannick Benezeth, Alamin Mansouri, and Julien Dubois. Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognition Letters, 124, pp. 82-90, https://doi.org/10.1016/j.patrec.2017.10.017
Ronny Stricker, Steffen Müller, and Horst-Michael Gross. Non-contact video-based pulse rate measurement on a mobile service robot. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 1056-1062. IEEE, 2014.
Daniel McDuff, Miah Wander, Xin Liu, Brian L Hill, Javier Hernandez, Jonathan Lester, and Tadas Baltrusaitis. Scamps: Synthetics for camera measurement of physiological signals. arXiv preprint arXiv:2206.04197, 2022.
Copyright (c) 2024 Адріан Наконечний, Ігор Бережний (Автор)
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