Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs

We here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals fr...

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Main Authors: Benjamin Caillet, Gilbert Maître, Steve Devènes, Darren Hight, Alessandro Mirra, Olivier L. Levionnois, Alena Simalatsar
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Medical Engineering
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Online Access:https://www.frontiersin.org/articles/10.3389/fmede.2024.1455116/full
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author Benjamin Caillet
Gilbert Maître
Steve Devènes
Darren Hight
Alessandro Mirra
Olivier L. Levionnois
Alena Simalatsar
author_facet Benjamin Caillet
Gilbert Maître
Steve Devènes
Darren Hight
Alessandro Mirra
Olivier L. Levionnois
Alena Simalatsar
author_sort Benjamin Caillet
collection DOAJ
description We here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals from a BIOPAC MP160 device in real-time. They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. Integrating the DoA estimation algorithm into a supporting toolbox allows for the clinical validation of the prediction and its immediate application in veterinary practice. This novel, artificial-intelligence-driven, user-defined, open-access software tool offers a valuable resource for both researchers and clinicians in conducting EEG analysis in real-time and offline settings in pigs and, potentially, other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.
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spelling doaj-art-e978d7b9348148f682565cc6070e96db2025-08-20T02:49:08ZengFrontiers Media S.A.Frontiers in Medical Engineering2813-687X2024-12-01210.3389/fmede.2024.14551161455116Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigsBenjamin Caillet0Gilbert Maître1Steve Devènes2Darren Hight3Alessandro Mirra4Olivier L. Levionnois5Alena Simalatsar6Institute of Systems Engineering, University of Applied Sciences and Arts - Western Switzerland, Sion, SwitzerlandInstitute of Systems Engineering, University of Applied Sciences and Arts - Western Switzerland, Sion, SwitzerlandInstitute of Systems Engineering, University of Applied Sciences and Arts - Western Switzerland, Sion, SwitzerlandDepartment of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, SwitzerlandDivision of Anaesthesiology and Pain Therapy, Vetsuisse Faculty, University of Bern, Bern, SwitzerlandDivision of Anaesthesiology and Pain Therapy, Vetsuisse Faculty, University of Bern, Bern, SwitzerlandInstitute of Systems Engineering, University of Applied Sciences and Arts - Western Switzerland, Sion, SwitzerlandWe here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals from a BIOPAC MP160 device in real-time. They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. Integrating the DoA estimation algorithm into a supporting toolbox allows for the clinical validation of the prediction and its immediate application in veterinary practice. This novel, artificial-intelligence-driven, user-defined, open-access software tool offers a valuable resource for both researchers and clinicians in conducting EEG analysis in real-time and offline settings in pigs and, potentially, other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.https://www.frontiersin.org/articles/10.3389/fmede.2024.1455116/fullEEG signal processingdepth of anesthesiaveterinary practicelong short-term memory modelpigs
spellingShingle Benjamin Caillet
Gilbert Maître
Steve Devènes
Darren Hight
Alessandro Mirra
Olivier L. Levionnois
Alena Simalatsar
Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
Frontiers in Medical Engineering
EEG signal processing
depth of anesthesia
veterinary practice
long short-term memory model
pigs
title Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
title_full Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
title_fullStr Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
title_full_unstemmed Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
title_short Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs
title_sort long short term memory based depth of anesthesia index computation for offline and real time clinical application in pigs
topic EEG signal processing
depth of anesthesia
veterinary practice
long short-term memory model
pigs
url https://www.frontiersin.org/articles/10.3389/fmede.2024.1455116/full
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