Key result
Rheoencephalography signals during apnea demonstrated significantly higher complexity using Fuzzy entropy compared to resting periods (p=0.0001).
Why the study?
Rheoencephalography monitors cerebral blood flow simply and inexpensively, but its ability to reflect cerebral blood flow changes has not been extensively proved.
Does rheoencephalography signal complexity, measured by entropy metrics, increase during apnea compared to resting periods in healthy subjects?
Observational (n=16)
Does rheoencephalography signal complexity, measured by entropy metrics, increase during apnea compared to resting periods in healthy subjects?
Entropy metrics, particularly Fuzzy entropy, can detect increased complexity in rheoencephalographic signals during apnea compared to resting periods, suggesting REG reflects cerebral blood flow changes.
Answers are grounded in this paper and cite their sources.
May support REG entropy for noninvasive CBF monitoring; hypothesis-generating and requires validation before clinical use.
Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.
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González et al. (2019) conducted an observational in Healthy (n=16). Apnea vs. Resting periods was evaluated on Complexity of REG signals measured by entropy metrics (e.g., FuzzyEn) (p=0.0001). Rheoencephalography signals during apnea demonstrated significantly higher complexity using Fuzzy entropy compared to resting periods (p=0.0001).
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