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: Jun 23, 2026

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
06:14

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

EEG under anesthesia--feature extraction with TESPAR.

Vasile V Moca1, Bertram Scheller, Raul C Mureşan

  • 1Romanian Institute of Science and Technology, Center for Cognitive and Neural Studies (Coneural), Str. Cireşilor nr. 29, 400487 Cluj-Napoca, Romania. moca@coneural.org

Computer Methods and Programs in Biomedicine
|April 18, 2009
PubMed
Summary
This summary is machine-generated.

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

Sharing the spotlight: Uncovering common attentional dynamics across species.

PLoS computational biology·2026
Same author

Adaptive rightward gaze shifts support configural object recognition during perceptual challenge.

iScience·2026
Same author

Beyond the first glance: How human presence enhances visual entropy and promotes spatial learning.

PLoS computational biology·2026
Same author

Mobile EEG (DreamMachine) and AI in Education: Toward Smarter Classrooms and Better Mental Health.

Studies in health technology and informatics·2025
Same author

Enhancing mobile EEG: Software development and performance insights of the DreamMachine.

HardwareX·2025
Same author

Butterfly effects in perceptual development: a review of the 'adaptive initial degradation' hypothesis.

Developmental review : DR·2025
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles

This study developed an automated system using Time Encoded Signal Processing And Recognition (TESPAR) to estimate depth of anesthesia (DOA) from EEG. The system accurately mimics human expert assessment, proving its clinical utility.

Area of Science:

  • Anesthesiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate depth of anesthesia (DOA) monitoring is crucial for patient safety during surgery.
  • Current methods rely on expert interpretation of electroencephalogram (EEG) signals, which can be subjective.
  • Objective and automated DOA estimation is needed to improve monitoring consistency.

Purpose of the Study:

  • To develop and validate an automated system for depth of anesthesia estimation using EEG.
  • To evaluate the performance of Time Encoded Signal Processing And Recognition (TESPAR) combined with multi-layer perceptrons for DOA classification.
  • To identify key EEG features contributing to accurate DOA estimation.

Main Methods:

  • Utilized Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique.

More Related Videos

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jun 23, 2026

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
06:14

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

  • Employed multi-layer perceptrons for classification of five distinct depth of anesthesia levels.
  • Trained and validated the system using EEG recordings assessed by human experts based on mid-latency auditory evoked potentials (MLAEPs) and clinical observations.

    • Main Results:

    • The automated system demonstrated performance closely mimicking human expert judgments.
    • Feature analysis revealed that DOA information is distributed across various frequency bands.
    • The inclusion of high frequencies (> 80 Hz), indicative of muscle activity, significantly benefited DOA detection.

    Conclusions:

    • The TESPAR-based system offers a viable and accurate method for automated depth of anesthesia estimation.
    • The findings highlight the importance of both frequency band information and high-frequency components in EEG for DOA assessment.
    • This automated approach has the potential to enhance the reliability and consistency of intraoperative anesthesia monitoring.