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Pulse decomposition analysis in photoplethysmography imaging
Vincent Fleischhauer1, Nora Ruprecht2, Michele Sorelli3
1University of Applied Sciences and Arts Dortmund, Dortmund, Germany.
Physiological Measurement
|October 6, 2020
Summary
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.
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.
