298
M. Thanaj et al.
in 15 healthy male volunteers. In this study signals were recorded initially with skin
temperature clamped at 33 °C and then when it was raised to 43 °C. As the epochs
are not synchronised, direct comparison between values at the two temperatures
in a given individual, or of group values, provides little understanding of spontaneous temporal activity. However, differences in the information content of the BF
signals are clearly observable, with that of signals measured at 43 °C being lower
(less variable) and having fewer unique states (a lower LZC) than those at 33 °C.
An LZC-index, calculated as the mean of the 15 × 40 s epochs, has been used to
discriminate between haemodynamic states or between “at-risk” groups [14, 17, 18].
However, to what extent LZ based algorithms can be used as a clinical tool with which
to classify disordered states within the microcirculation remains to be fully investigated. The cardiac and respiratory rhythms might be predicted to have significant
but opposite influences on signal complexity due to their generally periodic nature.
Skin sympathetic nerve activity is known to be modulated by respiration and cutaneous vasoconstrictor neurones are temporarily coupled to cardiac and respiratory
oscillations [26]. Heart rate variability may also contribute to the complexity of the
BF signal [72] and cardiac rhythm is modulated by respiration [76].
19.4.2 Entropy-Based and Effort to Compress Complexity
Analysis
Sample entropy (SampEn) provides an applicable finite sequence formulation that
discriminates the data sets by a measure of randomness, from totally regular to
completely random. SampEn assigns non-negative patterns in time series, with
larger values of entropy corresponding to more irregularity and smaller values corresponding to more regularity in the data. The regularity of the signal can be measured
with the SampEn, by defining how often a short time series is repeated. A similar
complexity method based on the lossless compression algorithm is the Effort to
Compress (ETC) complexity, proposed by Nagaraj, Balasubramanian [59]. Similar to
the LZC approach, the given sequence is also first converted to a symbolic sequence.
There have been few studies to investigate the SampEn and ETC complexity
of LDF signals from human skin blood. Figure 19.1a, c taken from Thanaj et al.
[83] gives one example of this. Such data provide strong evidence that, similar to
LZC, these algorithms showed sensitivity to a change in haemodynamic state. Here,
lower entropy and complexity indices with relatively consistent variation across the
15 epochs were observed when microvascular flow approached maximal dilation
and network perfusion during heating. However, Liao et al. [53] in a similar study
investigating the sample entropy indexes in response to local heating of the sacral skin
blood flow in people at risk of pressure ulcers, failed to find any significant correlation
with skin vasodilatory capacity. This led them to argue that nonlinear analysis is not
always a consistent method for the assessment of vasodilatory function.
M. Thanaj et al.
in 15 healthy male volunteers. In this study signals were recorded initially with skin
temperature clamped at 33 °C and then when it was raised to 43 °C. As the epochs
are not synchronised, direct comparison between values at the two temperatures
in a given individual, or of group values, provides little understanding of spontaneous temporal activity. However, differences in the information content of the BF
signals are clearly observable, with that of signals measured at 43 °C being lower
(less variable) and having fewer unique states (a lower LZC) than those at 33 °C.
An LZC-index, calculated as the mean of the 15 × 40 s epochs, has been used to
discriminate between haemodynamic states or between “at-risk” groups [14, 17, 18].
However, to what extent LZ based algorithms can be used as a clinical tool with which
to classify disordered states within the microcirculation remains to be fully investigated. The cardiac and respiratory rhythms might be predicted to have significant
but opposite influences on signal complexity due to their generally periodic nature.
Skin sympathetic nerve activity is known to be modulated by respiration and cutaneous vasoconstrictor neurones are temporarily coupled to cardiac and respiratory
oscillations [26]. Heart rate variability may also contribute to the complexity of the
BF signal [72] and cardiac rhythm is modulated by respiration [76].
19.4.2 Entropy-Based and Effort to Compress Complexity
Analysis
Sample entropy (SampEn) provides an applicable finite sequence formulation that
discriminates the data sets by a measure of randomness, from totally regular to
completely random. SampEn assigns non-negative patterns in time series, with
larger values of entropy corresponding to more irregularity and smaller values corresponding to more regularity in the data. The regularity of the signal can be measured
with the SampEn, by defining how often a short time series is repeated. A similar
complexity method based on the lossless compression algorithm is the Effort to
Compress (ETC) complexity, proposed by Nagaraj, Balasubramanian [59]. Similar to
the LZC approach, the given sequence is also first converted to a symbolic sequence.
There have been few studies to investigate the SampEn and ETC complexity
of LDF signals from human skin blood. Figure 19.1a, c taken from Thanaj et al.
[83] gives one example of this. Such data provide strong evidence that, similar to
LZC, these algorithms showed sensitivity to a change in haemodynamic state. Here,
lower entropy and complexity indices with relatively consistent variation across the
15 epochs were observed when microvascular flow approached maximal dilation
and network perfusion during heating. However, Liao et al. [53] in a similar study
investigating the sample entropy indexes in response to local heating of the sacral skin
blood flow in people at risk of pressure ulcers, failed to find any significant correlation
with skin vasodilatory capacity. This led them to argue that nonlinear analysis is not
always a consistent method for the assessment of vasodilatory function.