Literature DB >> 28347756

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

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

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, 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.
Copyright © 2017 Elsevier Inc. All rights reserved.

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

Mesh:

Year:  2017        PMID: 28347756     DOI: 10.1016/j.mvr.2017.03.008

Source DB:  PubMed          Journal:  Microvasc Res        ISSN: 0026-2862            Impact factor:   3.514


  3 in total

1.  Gaussian Modelling Characteristics of Peripheral Arterial Pulse: Difference between Measurements from the Three Trimesters of Healthy Pregnancy.

Authors:  Kunyan Li; Song Zhang; Lin Yang; Hongqing Jiang; Dongmei Hao; Lei Zhang; Dingchang Zheng
Journal:  J Healthc Eng       Date:  2018-10-11       Impact factor: 2.682

2.  Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.

Authors:  Zi-Juan Bi; Xing-Hua Yao; Xiao-Juan Hu; Pei Yuan; Xiao-Jing Guo; Zhi-Ling Guo; Si-Han Wang; Jun Li; Yu-Lin Shi; Jia-Cai Li; Ji Cui; Jia-Tuo Xu
Journal:  Evid Based Complement Alternat Med       Date:  2022-02-17       Impact factor: 2.629

3.  Evaluation of Cardiorespiratory Function During Cardiopulmonary Exercise Testing in Untreated Hypertensive and Healthy Subjects.

Authors:  Yahui Zhang; Zhihao Jiang; Lin Qi; Lisheng Xu; Xingguo Sun; Xinmei Chu; Yanling Liu; Tianjing Zhang; Stephen E Greenwald
Journal:  Front Physiol       Date:  2018-11-14       Impact factor: 4.566

  3 in total

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