Gaussian modelling method has been reported as a useful method to analyze arterial pulse waveform changes. This study aimed to provide scientific evidence on Gaussian modelling characteristics changes derived from the finger photoplethysmographic (PPG) pulses during exercise and recovery.65 healthy subjects (18 female and 47 male) were recruited. Finger PPG pulses were digitally recorded with 5 different exercise loads (0, 50, 75, 100, 125W) as well as during each of 4 minute (min) recovery period. The PPG pulses were normalized in both width and amplitude for each recording, which were decomposed into three independent Gaussian waves with nine parameters determined, including the peak amplitude (H1, H2, H3), peak time position (N1, N2, N3) and half-width (W1, W2, W3) from each Gaussian wave, and four extended parameters determined, including the peak time interval (T1,2, T1,3) and amplitude ratio (R1,2, R1,3) between 1st Gaussian wave and 2nd, 3rd Gaussian waves. These derived parameters were finally compared between different exercise loads and recovery phases.With gradually increased exercise loads, the peak amplitude H2, peak time position N1, N2, N3, and half-width W1, W2 increased, peak amplitude H3 decreased significantly (all P < 0.05). The peak time interval T1,2 and T1,3 increased significantly from 10.6 ± 1.2 and 36.0 ± 4.4 at rest to 14.4 ± 2.3 and 45.1 ± 6.5 at 100W exercise load, respectively (both P < 0.05). The amplitude ratio R1,2 also increased from 1.07 ± 0.2 at rest to 1.22 ± 0.2 at 100W, and the amplitude ratio R1,3 decreased from 1.10 ± 0.3 at rest to 0.42 ± 0.2 at 125W (all P < 0.05). An opposite changing trend of these parameters was observed during recovery phases.In conclusion, this study has quantitatively demonstrated significant changes of Gaussian modelling characteristics derived from finger PPG pulse with exercise and during recovery, providing scientific evidence for the physiological mechanism that exercise increases cardiac ejection and vasodilation, and reduces the total peripheral vascular resistance.
Bibliographical noteNOTICE: this is the author’s version of a work that was accepted for publication in Microvascular Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Microvascular Research, 116 (2018) DOI: 10.1016/j.mvr.2017.03.008
© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
- Exercise load
- Gaussian pulse decomposition
- Pulse wave analysis