Fourier and wavelet analysis of skin laser doppler flowmetry signals
University of Southampton, School of Engineering Sciences,
This thesis examines the measurement of skin microvascular blood flows from Laser Doppler Flowmetry (LDF) signals. Both healthy subjects and those with features of the metabolic syndrome are studied using signal processing techniques such as the Fourier and Wavelet transforms. An aim of this study is to investigate whether change in blood
flow at rest can be detected from the spectral content of the processed signals in the diferent subject groups. Additionally the effect of insulin is examined via hyperinsulinemic euglycemic clamp together with measurements made from a low power 1mW, standard separation (0.5mm) probe and a high power 20mW, wide separation (4mm)
Research design and Methods
We studied a cohort of individuals with 3 or more features (obesity, insulin resistance, etc) of metabolic syndrome (MS) as group 1 (n = 17), and also second measurements of the same subjects taken 6 months later as group 2 (n = 12 because not every subject in group 1 participated a second measurement). Our study also included 3 healthy people as the healthy group. Skin blood flow was recorded using LDF monitoring device at rest and in response to insulin during hyperinsulinemic euglycemic clamp. We used fast Fourier transform (FFT) and Wavelet transform (WT) based methods to assess skin blood flow and developed models to characterize insulin-induced blood flow changes.
We demonstrated the application of Fourier and Wavelet analysis in analyzing LDF skin blood signals. For group 1 subjects, by using power spectral density (PSD) and average scalogram, we showed changes of blood flow in response to insulin during hyperinsulinemic euglycemic clamp in all five characteristic frequency bands are not statistically signifcant. Between group 1 and the healthy group, changes in relative spectral power contributions of some frequency components are statistically signifcant. We constructed a time-evolution model derived from WT scalogram, and this can be used to study the time-evolutionary changes of the endothelial activity in response to insulin. A preliminary analysis of endothelial activities (pre, low, high insulin) in the time-evolution model is attempted using multiple sinusoidal fitting, the dominant amplitude term has an oscillation of 0.005 rad/s with very small standard deviation, and the less dominant amplitude oscillation has an oscillation of about 0.0127 rad/s. However, we find it difficult to interpret these oscillations physiologically.
FFT (spectral analysis) and WT (scalogram) based methods together with statistics can be adequately used to investigate controls of skin blood flow by detecting the frequency content of LDF signals. Wavelet analysis has the advantage of obtaining better frequency resolution for lower frequency components (e.g., endothelial activity).
In this cohort of individuals with central obesity who are at risks of developing cardiovascular diseases (CVD), our analyses show that insulin-induced vasodilatory effects are impaired.
||University of Southampton, Computational Engineering & Design Group
||29 Mar 2012 15:28
||17 Apr 2017 17:27
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