Abstract
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.
Highlights
The brain represents only up to 2% of the body weight in humans, while it receives up to 20% of the total cardiac output [1]
The evolution of the entropy metrics approximate entropy (ApEn)(m,r,N), sample entropy (SampEn)(m,r,N), fuzzy entropy (FuzzyEn)(m,n,r,N), Shannon entropy (SE)(N,m,ε), corrected conditional entropy (CCE)(N,m,ε) and ρ(N,ε) as a function of the parameters selection is presented, as well as their ability to differentiate between apnea and baseline signals
The entropy CCE and the regularity index ρ resulted in statistically significant results for apnea detection, while none of the parameter combinations tested for SE was able to identify apneas
Summary
The brain represents only up to 2% of the body weight in humans, while it receives up to 20% of the total cardiac output [1]. This suggests that the brain has large metabolic needs and, as it is an organ that has no mechanism to store nutrients, oxygen or water, it needs to receive a large and uninterrupted blood supply. Cerebral ischemia and neuronal damage are two critical adverse events during anesthesia. In this way, even though encephalic vascular accidents are infrequent during common surgeries, complex procedures present a higher risk [3]. It is a Entropy 2019, 21, 605; doi:10.3390/e21060605 www.mdpi.com/journal/entropy
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