Comparing the spectral components of laser Doppler flowmetry and photoplethysmography signals for the assessment of the vascular response to hyperoxia

Journal Biomedical and Biopharmaceutical Research (BBR)

Mariana Bento¹  Henrique Silva²  Helena Vieira³  L. Monteiro Rodrigues³ 

[1] Pharmacol. Sc Depart — Universidade de Lisboa, Faculty of Pharmacy;CBIOS — Universidade Lusófona's Research Center for Biosciences and Health Technologies;Pharmacol. Sc Depart — Universidade de Lisboa, Faculty of Pharmacy;

关键词: component analysis;    wavelet transform;    physiological signal;    LDF;    PPG;   

DOI  :  10.19277/bbr.14.2.161

来源: DOAJ

【 摘 要 】

The skin is an ideal organ to record complex vascular signals, with laser Doppler flowmetry (LDF) and photop- lethysmography (PPG) being commonly used quantification techniques. Hyperoxia is a common provocation stim- ulus, although many questions remain unanswered regarding its effect on microcirculation. In recent years much attention has been given to the mathematical modelling of physiological signals, with component analysis being a common approach. We aimed to characterize the LDF and PPG spectral profiles using wavelet transform (WT) to evaluate their components’ response to a 100% oxygen provocation on skin microcirculation. We recorded LDF and PPG signals from the toes of 10 healthy subjects (20.5 ± 3.1 years old, after giving informed consent) before, during and after breathing an atmosphere of 100% oxygen. Signals were decomposed in their main components with the WT. Six components were identified in both signals (cardiac, respiratory, myogenic, sympathetic, endothelial NOdependent and endothelial NO-independent) in identical spectral positions. The cardiac, respiratory and myogenic activities increased during hyperoxia in both signals, while endothelial activities gave different responses. These discrepancies suggest that these techniques measure different phenomena and, therefore, may not be entirely inter- changeable. These results reinforce the usefulness of WT in decomposing complex physiological signals.

【 授权许可】

Unknown