Entropy measures as descriptors to identify apneas in rheoencephalographic signals
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...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/172556 |
| Acesso em linha: | https://hdl.handle.net/2117/172556 https://dx.doi.org/10.3390/e21060605 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sleep apnea syndromes Entropy Brain Cerebral blood flow Rheoencephalography Apnea detection Complexity Approximate entropy (ApEn) Sample entropy (SampEn) Fuzzy entropy (FuzzyEn) Corrected conditional entropy (CCE) Shannon entropy (SE) Síndromes d'apnea del son Cervell Entropia Àrees temàtiques de la UPC::Enginyeria biomèdica::Robòtica mèdica |
| Resumo: | 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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