Gaussian modelling characteristics changes derived from finger photoplethysmographic pulses during exercise and recovery

Anran Wang; Lin Yang; Weimin Wen; Song Zhang; Guanxiong Gu; Dingchang Zheng

doi:10.1016/j.mvr.2017.03.008

Gaussian modelling characteristics changes derived from finger photoplethysmographic pulses during exercise and recovery

Microvasc Res. 2018 Mar:116:20-25. doi: 10.1016/j.mvr.2017.03.008. Epub 2017 Mar 24.

Authors

Anran Wang¹, Lin Yang², Weimin Wen¹, Song Zhang¹, Guanxiong Gu¹, Dingchang Zheng³

Affiliations

¹ College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China.
² College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China. Electronic address: yanglin@bjut.edu.cn.
³ Health and Wellbeing Academy, Faculty of Medical Science, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.

PMID: 28347756
DOI: 10.1016/j.mvr.2017.03.008

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 (T_1,2, T_1,3) and amplitude ratio (R_1,2, R_1,3) 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 T_1,2 and T_1,3 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 R_{1,2 also} increased from 1.07±0.2 at rest to 1.22±0.2 at 100W, and the amplitude ratio R_1,3 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.

Keywords: Exercise load; Gaussian pulse decomposition; PPG; Pulse wave analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Arterial Pressure
Cardiac Output
Exercise / physiology*
Female
Fingers / blood supply*
Hemodynamics*
Humans
Male
Models, Cardiovascular*
Photoplethysmography / methods*
Predictive Value of Tests
Pulse Wave Analysis / methods*
Recovery of Function
Signal Processing, Computer-Assisted*
Time Factors
Vascular Resistance
Vasodilation
Young Adult