TY - JOUR
T1 - Separation of responsive and unresponsive patients under clinical conditions
T2 - comparison of symbolic transfer entropy and permutation entropy
AU - Zanner, Robert
AU - Berger, Sebastian
AU - Schröder, Natalie
AU - Kreuzer, Matthias
AU - Schneider, Gerhard
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2024/2
Y1 - 2024/2
N2 - Electroencephalogram (EEG)-based monitoring during general anesthesia may help prevent harmful effects of high or low doses of general anesthetics. There is currently no convincing evidence in this regard for the proprietary algorithms of commercially available monitors. The purpose of this study was to investigate whether a more mechanism-based parameter of EEG analysis (symbolic transfer entropy, STE) can separate responsive from unresponsive patients better than a strictly probabilistic parameter (permutation entropy, PE) under clinical conditions. In this prospective single-center study, the EEG of 60 surgical ASA I–III patients was recorded perioperatively. During induction of and emergence from anesthesia, patients were asked to squeeze the investigators’ hand every 15s. Time of loss of responsiveness (LoR) during induction and return of responsiveness (RoR) during emergence from anesthesia were registered. PE and STE were calculated at −15s and +30s of LoR and RoR and their ability to separate responsive from unresponsive patients was evaluated using accuracy statistics. 56 patients were included in the final analysis. STE and PE values decreased during anesthesia induction and increased during emergence. Intra-individual consistency was higher during induction than during emergence. Accuracy values during LoR and RoR were 0.71 (0.62–0.79) and 0.60 (0.51–0.69), respectively for STE and 0.74 (0.66–0.82) and 0.62 (0.53–0.71), respectively for PE. For the combination of LoR and RoR, values were 0.65 (0.59–0.71) for STE and 0.68 (0.62–0.74) for PE. The ability to differentiate between the clinical status of (un)responsiveness did not significantly differ between STE and PE at any time. Mechanism-based EEG analysis did not improve differentiation of responsive from unresponsive patients compared to the probabilistic PE. Trial registration: German Clinical Trials Register ID: DRKS00030562, November 4, 2022, retrospectively registered.
AB - Electroencephalogram (EEG)-based monitoring during general anesthesia may help prevent harmful effects of high or low doses of general anesthetics. There is currently no convincing evidence in this regard for the proprietary algorithms of commercially available monitors. The purpose of this study was to investigate whether a more mechanism-based parameter of EEG analysis (symbolic transfer entropy, STE) can separate responsive from unresponsive patients better than a strictly probabilistic parameter (permutation entropy, PE) under clinical conditions. In this prospective single-center study, the EEG of 60 surgical ASA I–III patients was recorded perioperatively. During induction of and emergence from anesthesia, patients were asked to squeeze the investigators’ hand every 15s. Time of loss of responsiveness (LoR) during induction and return of responsiveness (RoR) during emergence from anesthesia were registered. PE and STE were calculated at −15s and +30s of LoR and RoR and their ability to separate responsive from unresponsive patients was evaluated using accuracy statistics. 56 patients were included in the final analysis. STE and PE values decreased during anesthesia induction and increased during emergence. Intra-individual consistency was higher during induction than during emergence. Accuracy values during LoR and RoR were 0.71 (0.62–0.79) and 0.60 (0.51–0.69), respectively for STE and 0.74 (0.66–0.82) and 0.62 (0.53–0.71), respectively for PE. For the combination of LoR and RoR, values were 0.65 (0.59–0.71) for STE and 0.68 (0.62–0.74) for PE. The ability to differentiate between the clinical status of (un)responsiveness did not significantly differ between STE and PE at any time. Mechanism-based EEG analysis did not improve differentiation of responsive from unresponsive patients compared to the probabilistic PE. Trial registration: German Clinical Trials Register ID: DRKS00030562, November 4, 2022, retrospectively registered.
KW - EEG monitoring
KW - Frontal-parietal connectivity
KW - General anesthesia
KW - Permutation entropy
KW - Symbolic transfer entropy
UR - http://www.scopus.com/inward/record.url?scp=85164826493&partnerID=8YFLogxK
U2 - 10.1007/s10877-023-01046-w
DO - 10.1007/s10877-023-01046-w
M3 - Article
C2 - 37436600
AN - SCOPUS:85164826493
SN - 1387-1307
VL - 38
SP - 187
EP - 196
JO - Journal of Clinical Monitoring and Computing
JF - Journal of Clinical Monitoring and Computing
IS - 1
ER -