Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 10, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Forecasting Excessive Anesthesia Depth Using EEG α-Spindle Dynamics and Machine Learning.

Christophe Sun, Pierre-Olivier Michel, Francois David

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 8, 2026
    PubMed
    Summary

    This study introduces a new EEG-based method using alpha-spindle dynamics to predict anesthetic drug overdosage. The framework accurately forecasts isoelectric suppression, enabling proactive anesthesia management.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Slow intrinsic oscillations in the ventrolateral preoptic nucleus.

    iScience·2026
    Same author

    Graph-based analysis of volumetric image data reveals predominant layer Va-to-II/III feedback in mouse motor cortex.

    Cell reports·2026
    Same author

    Capillary refill time changes are associated with Vascular Waterfall response in post-cardiac surgery patients.

    Annals of intensive care·2026
    Same author

    Cardiovascular safety profile of prostaglandin E and prostacyclin analogues: Clinical reports and ex vivo studies on human coronary arteries.

    Prostaglandins, leukotrienes, and essential fatty acids·2026
    Same author

    Portal vein Doppler in peri-operative and critical care medicine: Physiology, measurement and clinical applications.

    European journal of anaesthesiology·2026
    Same author

    Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca<sup>2+</sup> Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus.

    Glia·2026

    Area of Science:

    • Neuroscience
    • Anesthesiology
    • Biomedical Engineering

    Background:

    • Accurate prediction of anesthetic drug overdosage is crucial for patient safety during general anesthesia.
    • Identifying relevant electroencephalogram (EEG) indicators is key to anticipating anesthesia depth changes.

    Purpose of the Study:

    • To develop a real-time, data-driven framework for predicting anesthetic drug overdosage using EEG signals.
    • To identify and utilize alpha-spindle dynamics from frontal EEG recordings as predictive indicators.

    Main Methods:

    • Utilized Empirical Mode Decomposition to segment transient alpha-spindle events from EEG data.
    • Extracted statistical features (amplitude, duration, frequency, suppression intervals) of alpha spindles.
    • Trained a Light Gradient Boosting Machine (LGBM) classifier on a clinical EEG dataset covering anesthesia phases.

    Related Experiment Videos

    Last Updated: Jul 10, 2026

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
    04:13

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

    Published on: November 13, 2019

    Main Results:

    • The LGBM model achieved over 80% accuracy in classifying anesthesia phases (induction, maintenance, emergence).
    • The model accurately predicted isoelectric suppression (a marker of overdosage) with 96% accuracy, up to 90 seconds in advance.
    • Alpha-spindle based metrics offered a non-invasive, interpretable, and predictive approach.

    Conclusions:

    • The developed real-time EEG framework can forecast unintentional anesthetic drug overdosage.
    • This method enables proactive anesthesia management based solely on EEG signals.
    • Integration into depth of anesthesia (DoA) monitoring models can enhance clinical practice and patient safety.