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Pulse decomposition analysis in photoplethysmography imaging.

Vincent Fleischhauer1, Nora Ruprecht2, Michele Sorelli3

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Pulse wave decomposition (PWD) algorithms are effective for morphological analysis in photoplethysmography imaging (PPGI). Two-kernel algorithms show superior robustness against noise and motion artifacts, enabling new PPGI applications.

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Area of Science:

  • Biomedical Engineering
  • Physiological Measurement

Background:

  • Photoplethysmography imaging (PPGI) is gaining attention for its non-invasive capabilities.
  • Morphological analysis of PPGI signals has been underexplored.
  • Pulse wave decomposition (PWD) offers a method for analyzing pulse wave morphology.

Purpose of the Study:

  • To investigate the applicability of various PWD algorithms for morphological analysis in PPGI.
  • To compare PWD algorithm performance regarding robustness to noise and motion artifacts.
  • To assess the ability of PWD to reveal physiological changes detected by PPGI.

Main Methods:

  • Comparison of PWD algorithms using simulated and experimental PPGI data.
  • Evaluation of algorithm robustness against noise and motion artifacts.
  • Assessment of morphological information preservation and physiological change detection.

Main Results:

  • PWD algorithms combining Gamma and Gaussian distributions demonstrated superior performance.
  • Two-kernel PWD algorithms exhibited the highest robustness to noise and motion artifacts (14.09% improvement).
  • PWD successfully revealed physiological changes in response to distal stimuli via PPGI.

Conclusions:

  • Pulse decomposition analysis is feasible for PPGI, especially with low-kernel algorithms, opening novel applications.
  • Findings highlight differences between PWD algorithms and the importance of initial values for future research.
  • Released algorithms and simulated data to support further research in PWD and PPGI.