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 Videos

A method for the automatic detection of arousals during sleep.

F De Carli1, L Nobili, P Gelcich

  • 1Center for Cerebral Neurophysiology, National Research Council, Genoa, Italy. fabrizio@dism.unige.it

Sleep
|August 18, 1999
PubMed
Summary

This study presents an automated method for detecting arousals in sleep studies using wavelet transforms on EEG and EMG data. The system demonstrated higher sensitivity than human experts, suggesting improved feasibility for arousal analysis.

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

When the Solution May Be Worse Than the Problem: A Caution Against Permanent Daylight Saving Time, on Behalf of the Italian Academy of Sleep Medicine.

Journal of sleep research·2026
Same author

Negative MRI and a seizure onset zone close to eloquent areas in FCD type II: Application of MRg-LiTT after a SEEG re-evaluation in pediatric patients with a previous failed surgery.

Epilepsy & behavior : E&B·2024
Same author

Modules in connectomes of phase-synchronization comprise anatomically contiguous, functionally related regions.

NeuroImage·2023
Same author

SAS CARE 1: Sleep architecture changes in a cohort of patients with Ischemic Stroke/TIA.

Sleep medicine·2022
Same author

Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters.

Brain stimulation·2022
Same author

Scoliosis with cognitive impairment in a girl with 8q11.21q11.23 microdeletion and SNTG1 disruption.

Bone·2021

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Arousals during sleep are critical indicators of sleep quality and disorders.
  • Manual scoring of arousals in polysomnography is time-consuming and subjective.
  • Digital polysomnography offers potential for automated analysis.

Purpose of the Study:

  • To develop and validate a computer-based method for automatic arousal detection in digital polysomnographic recordings.
  • To compare the performance of the automated system against human expert scoring.
  • To assess the feasibility of automated arousal analysis in clinical and research settings.

Main Methods:

  • Utilized wavelet transform for time-frequency analysis of EEG signals.
  • Incorporated EMG data and multichannel, context-sensitive analysis for arousal identification.

Related Experiment Videos

  • Trained the system on a subset of recordings and tested on a larger set, with expert validation.

    • Main Results:

    • The automated system achieved a sensitivity of 88.1%, surpassing human experts (72.4% and 78.4%).
    • Selectivity for the automated system was 74.5%, compared to experts (83.0% and 82.0%).
    • A reference set of 1125 definite arousals and 266 uncertain events was established for validation.

    Conclusions:

    • Automated arousal detection shows promise for increasing the feasibility and efficiency of sleep analysis.
    • Combining automated detection with expert validation may enhance the study of arousal characteristics.
    • The developed system offers a valuable tool for objective sleep arousal assessment.