2026-04-19T09:56:14Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1517052025-12-04T20:39:28Zcom_2072_1057col_2072_478800col_2072_478917
RECERCAT
author
Valencia, José Fernando
author
Melia, Umberto
author
Vallverdú, Montserrat
author
Borrat, Xavier
author
Jospin, Mathieu
author
Jensen, Erik W.
author
Porta, Alberto
author
Gambús Cerrillo, Pedro Luis
author
Caminal, Pere
2020-03-02T17:18:56Z2020-03-02T17:18:56Z2016-03-182020-03-02T17:18:56Z
The level of sedation in patients undergoing medical procedures is decided to assure unconsciousness and prevent pain. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work was to analyze the capability of prediction of nociceptive responses based on refined multiscale entropy (RMSE) and auto mutual information function (AMIF) applied to EEG signals recorded in 378 patients scheduled to undergo ultrasonographic endoscopy under sedation-analgesia. Two observed categorical responses after the application of painful stimulation were analyzed: the evaluation of the Ramsay Sedation Scale (RSS) after nail bed compression and the presence of gag reflex (GAG) during endoscopy tube insertion. In addition, bispectrum (BIS), heart rate (HR), predicted concentrations of propofol (CeProp) and remifentanil (CeRemi) were annotated with a resolution of 1 s. Results showed that functions based on RMSE, AMIF, HR and CeRemi permitted predicting different stimulation responses during sedation better than BIS.
cc-by (c) Valencia, José F. et al., 2016 http://creativecommons.org/licenses/by/3.0/es info:eu-repo/semantics/openAccess
Entropia (Teoria de la informació)
Sedants
Dolor
Electroencefalografia
Entropy (Information theory)
Sedatives
Pain
Electroencephalography
Assessment of Nociceptive Responsiveness Levels during Sedation-Analgesia by Entropy Analysis of EEG.
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion