Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring.

Journal: Journal of clinical monitoring and computing
PMID: 38573367

Abstract

PURPOSE: Neuromuscular monitoring is frequently plagued by artefacts, which along with the frequent unawareness of the principles of this subtype of monitoring by many clinicians, tends to lead to a cynical attitute by clinicians towards these monitors. As such, the present study aims to derive a feature set and evaluate its discriminative performance for the purpose of Train-of-Four Ratio (TOF-R) outlier analysis during continuous intraoperative EMG-based neuromuscular monitoring.

Authors

  • Michaël Verdonck
    Department of Business Informatics and Operations Management, University Ghent, Tweekerkenstraat 2, Ghent, 9000, Belgium. michael.verdonck@multipitch.be.
  • Hugo Carvalho
    Department of Anesthesia and Perioperative Medicine, Universitair Ziekenhuis Brussel, Jette, Belgium.
  • Thomas Fuchs-Buder
    University of Lorraine, Centre Hospitalier Universitaire de Nancy/Hôpitaux de Brabois, Lorraine, France.
  • Sorin J Brull
    Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA.
  • Jan Poelaert
    Department of Anesthesia, AZ Maria Middelares Gent, Ghent, Belgium.

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