26 Phase Coherence Between Cardiovascular Oscillations in Malaria …
409
Subsequently, this is accomplished in an iterative fashion, simply by using the appropriate value and re-scaling the distribution to substitute Fourier amplitudes, which
allows us to obtain resemblance between the distributions and power spectra of the
surrogates and the original signals. At each frequency we took the coherence threshold to be 95% of the highest value of 100 random realisations of IAAFT surrogates.
Finally, the effective/significant coherence was estimated by subtracting the 95th
percentile of the 100 surrogate values, thus yielding the extent to which the phases
of the two signals at each frequency are correlated.
26.3.4 Statistical analysis
Non-parametric statistical tests were used, implying that no assumptions were being
made about any underlying distributions, thus allowing robust conclusions to be
drawn. The Wilcoxon rank sum test [43] was used to test for possibly significant
differences between blood flow and other signals measured from malarial and control
subjects, respectively, as the corresponding time-series do not match. The Wilcoxon
rank sum test is used to determine whether two unmatched samples come from similar
distributions, whilst the sign rank test requires that the samples are matched. In all
cases, p < 0.05 was considered as being statistically significant.
26.4 Results
26.4.1 Effect of Malaria on Blood Pressure, Respiration
Frequency, and Skin Temperature
As summarised in Table 26.1, the FM group differed from the NM group in all
parameters, including skin and core temperatures, heart and respiratory rates, systolic
and diastolic blood pressures, and blood packed cell volume. The NFM group, on the
other hand, differed in some parameters but not in all. FM differed from NFM in all
parameters except SBP and PCV. NFM differed from NM only in core temperature
and IHR.
26.4.2 Detecting Oscillations Using Time-Frequency Analysis
The signals were transformed to the time-frequency domain by wavelet analysis with
a Morlet wavelet of f 0 = 1.5 Hz, using custom Matlab codes. The time-averaged
power was then calculated and normalised in each case. Note that, even where the
time-averaged values of two signals are the same, computation of their time-evolving
409
Subsequently, this is accomplished in an iterative fashion, simply by using the appropriate value and re-scaling the distribution to substitute Fourier amplitudes, which
allows us to obtain resemblance between the distributions and power spectra of the
surrogates and the original signals. At each frequency we took the coherence threshold to be 95% of the highest value of 100 random realisations of IAAFT surrogates.
Finally, the effective/significant coherence was estimated by subtracting the 95th
percentile of the 100 surrogate values, thus yielding the extent to which the phases
of the two signals at each frequency are correlated.
26.3.4 Statistical analysis
Non-parametric statistical tests were used, implying that no assumptions were being
made about any underlying distributions, thus allowing robust conclusions to be
drawn. The Wilcoxon rank sum test [43] was used to test for possibly significant
differences between blood flow and other signals measured from malarial and control
subjects, respectively, as the corresponding time-series do not match. The Wilcoxon
rank sum test is used to determine whether two unmatched samples come from similar
distributions, whilst the sign rank test requires that the samples are matched. In all
cases, p < 0.05 was considered as being statistically significant.
26.4 Results
26.4.1 Effect of Malaria on Blood Pressure, Respiration
Frequency, and Skin Temperature
As summarised in Table 26.1, the FM group differed from the NM group in all
parameters, including skin and core temperatures, heart and respiratory rates, systolic
and diastolic blood pressures, and blood packed cell volume. The NFM group, on the
other hand, differed in some parameters but not in all. FM differed from NFM in all
parameters except SBP and PCV. NFM differed from NM only in core temperature
and IHR.
26.4.2 Detecting Oscillations Using Time-Frequency Analysis
The signals were transformed to the time-frequency domain by wavelet analysis with
a Morlet wavelet of f 0 = 1.5 Hz, using custom Matlab codes. The time-averaged
power was then calculated and normalised in each case. Note that, even where the
time-averaged values of two signals are the same, computation of their time-evolving