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ORIGINAL RESEARCH article

Front. Med. Eng.
Sec. Computational Medicine
Volume 2 - 2024 | doi: 10.3389/fmede.2024.1455116

Long Short-Term Memory Based Depth of Anaesthesia Index Computation for Offline and Real-Time Clinical Application in Pigs

Provisionally accepted
Alena Simalatsar Alena Simalatsar 1*Benjamin Caillet Benjamin Caillet 1Gilbert Maître Gilbert Maître 1Steve Devenes Steve Devenes 1Darren F Hight Darren F Hight 2,3*Alessandro Mirra Alessandro Mirra 2*Olivier L Levionnois Olivier L Levionnois 2
  • 1 HES-SO Valais-Wallis, Sion, Switzerland
  • 2 University of Bern, Bern, Bern, Switzerland
  • 3 Inselspital University Hospital Bern, Bern, Bern, Switzerland

The final, formatted version of the article will be published soon.

    In this work, we present a Deep Learning approach for computing DoA for pigs undergoing general anesthesia with propofol, integrated into a novel General Anesthesia specialized Matlab-based Graphical User Interface (GAM-GUI) toolbox. The GAM-GUI toolbox allows for the collection of EEG signals from a BIOPAC MP160 device in real-time, their analysis 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 intelligent 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 potential for other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.

    Keywords: EEG signal processing, Depth of anaesthesia, Veterinary practice, Long-short term memory model, pigs

    Received: 26 Jun 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Simalatsar, Caillet, Maître, Devenes, Hight, Mirra and Levionnois. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Alena Simalatsar, HES-SO Valais-Wallis, Sion, Switzerland
    Darren F Hight, University of Bern, Bern, 3012, Bern, Switzerland
    Alessandro Mirra, University of Bern, Bern, 3012, Bern, Switzerland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.