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1
On the Application of Parsimonious Models for Surgical Anes..:
, In:
2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
,
Nsugbe, Ejay
;
Asogbon, Mojisola Grace
;
Samuel, Oluwarotimi Williams
. - p. 131-136 , 2022
Link:
https://doi.org/10.1109/MetroInd4.0IoT54413.2022.98317..
RT T1 <cTitle>2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)</cTitle>: T1 <Title>On the Application of Parsimonious Models for Surgical Anesthesia Depth Prediction using EEG Recordings</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9831706&Exemplar=1&LAN=DE A1 Nsugbe, Ejay A1 Asogbon, Mojisola Grace A1 Samuel, Oluwarotimi Williams A1 Li, Guanglin YR 2022 K1 Uncertainty K1 Surgery K1 Predictive models K1 Brain modeling K1 Feature extraction K1 Anesthesia K1 Electroencephalography K1 Artificial Intelligence K1 EEG K1 Signal Processing K1 Multiresolution K1 Patient Safety SP 131 OP 136 LK http://dx.doi.org/https://doi.org/10.1109/MetroInd4.0IoT54413.2022.9831706 DO https://doi.org/10.1109/MetroInd4.0IoT54413.2022.9831706 SF ELIB - SuUB Bremen
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