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Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

1
Biomedical Engineering Research Centre, Universitat Politècnica de Catalunya, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 08028 Barcelona, Spain
2
Quantium Medical, Research and Development Department, 08302 Mataró, Spain
3
Systems Pharmacology Effect Control & Modeling (SPEC-M) Research Group, Department of Anesthesia, Hospital CLINIC de Barcelona, 08036 Barcelona, Spain
4
Department of Anesthesia and Perioperative Care, University of California San Francisco (UCSF), San Francisco, CA 94143, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(6), 605; https://doi.org/10.3390/e21060605
Received: 27 April 2019 / Revised: 9 June 2019 / Accepted: 15 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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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. View Full-Text
Keywords: cerebral blood flow; rheoencephalography; apnea detection; complexity; approximate entropy (ApEn); sample entropy (SampEn); fuzzy entropy (FuzzyEn); corrected conditional entropy (CCE); Shannon entropy (SE) cerebral blood flow; rheoencephalography; apnea detection; complexity; approximate entropy (ApEn); sample entropy (SampEn); fuzzy entropy (FuzzyEn); corrected conditional entropy (CCE); Shannon entropy (SE)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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González, C.; Jensen, E.; Gambús, P.; Vallverdú, M. Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals. Entropy 2019, 21, 605.

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