title: Gaussian modelling characteristics changes derived from finger photoplethysmographic pulses during exercise and recovery.
abstract: 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 4minute (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, W2increased, peak amplitude H3decreased significantly (all P<0.05). The peak time interval T1,2and T1,3increased 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 alsoincreased from 1.07±0.2 at rest to 1.22±0.2 at 100W, and the amplitude ratio R1,3decreased 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.
authors.affiliation: College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Anran
authors.initials: A
authors.lastname: Wang
authors.affiliation: College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China. Electronic address: yanglin@bjut.edu.cn.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.affiliation: College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Weimin
authors.initials: W
authors.lastname: Wen
authors.affiliation: College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.affiliation: College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China.
authors.forename: Guanxiong
authors.initials: G
authors.lastname: Gu
authors.affiliation: Health and Wellbeing Academy, Faculty of Medical Science, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.
authors.forename: Dingchang
authors.initials: D
authors.lastname: Zheng
mesh_terms: D000328:Adult; D062186:Arterial Pressure; D002302:Cardiac Output; D015444:Exercise / Q000502:physiology*; D005260:Female; D005385:Fingers / Q000098:blood supply*; D006439:Hemodynamics; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular; D017156:Photoplethysmography / Q000379:methods*; D011237:Predictive Value of Tests; D063177:Pulse Wave Analysis / Q000379:methods*; D020127:Recovery of Function; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014655:Vascular Resistance; D014664:Vasodilation; D055815:Young Adult
keywords: Exercise load; Gaussian pulse decomposition; PPG; Pulse wave analysis