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

• Published in: Frontiers in medical engineering 2024-12, Vol.2
• Main Authors: Benjamin Caillet, Gilbert Maître, Steve Devènes, Darren Hight, Alessandro Mirra, Olivier L. Levionnois, Alena Simalatsar
• Format: Article
• Language: English
• Subjects: depth of anesthesia; EEG signal processing; long short-term memory model; pigs; veterinary practice
• ISSN: 2813-687X
• DOI: 10.3389/fmede.2024.1455116

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.