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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs.

EBioMedicine·2026
Same author

Age-related directional asymmetry in the rod-and-frame test.

Frontiers in aging neuroscience·2026
Same author

The Amsler Grid in Everyday Practice: A Review of Its Role and Limitations in Primary Care.

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Nerve Conduction Study and Functional Assessment After Upper Extremity Macroreplantation.

Journal of clinical medicine·2025
Same author

Altered Sleep Patterns in Wilson's Disease Including Shortened REM Latency.

Diagnostics (Basel, Switzerland)·2025
Same author

Distance-Dependent Distribution of Biomarkers in Colorectal Cancer Tissues: In Vivo Study.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Jul 24, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.4K

EEG phase synchronization during absence seizures.

Pawel Glaba1, Miroslaw Latka1, Małgorzata J Krause2

  • 1Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland.

Frontiers in Neuroinformatics
|July 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using EEG phase synchronization and machine learning to detect absence seizures, identifying seizure disorganization. The detector shows high accuracy and potential for unobtrusive headbands.

Keywords:
absence seizurechildhood absence epilepsyepilepsyjuvenile absence epilepsyseizure detectionseizure fragmentationsynchronizationwavelets

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

Related Experiment Videos

Last Updated: Jul 24, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.4K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

Area of Science:

  • Neuroscience
  • Epileptology
  • Signal Processing

Background:

  • Childhood absence epilepsy (CAE) and juvenile absence epilepsy (JAE) are characterized by generalized rhythmic spike-and-wave discharges (SWDs).
  • Existing absence detection algorithms focus on individual SWDs, potentially missing seizure disorganization.
  • Pathological neuronal hypersynchrony in absence seizures presents a challenge for accurate detection and characterization.

Purpose of the Study:

  • To investigate EEG phase synchronization for detecting absence seizures and quantifying their fragmentation.
  • To develop and evaluate a machine learning classifier using phase synchronization and amplitude features for SWD detection.
  • To assess the detector's performance in real-time data streams and its utility for distinguishing seizure characteristics.

Main Methods:

  • Analysis of EEG phase synchronization using the wavelet phase synchronization index in patients with CAE/JAE and healthy subjects.
  • Development of a machine learning classifier incorporating phase synchronization and normalized amplitude from 1-second EEG segments with 0.5-second overlap.
  • Validation of the detector using 19-channel (10-20 setup) and a reduced 6-channel (Fp1, Fp2, F7, F8, O1, O2) configuration.

Main Results:

  • The developed detector achieved 99.2% accuracy in identifying absences using 19 channels, with 83% overlap of classified ictal segments.
  • Seizure disorganization was identified in approximately half of the 65 subjects, with generalized SWDs comprising about 80% of abnormal EEG activity.
  • The detector demonstrated good performance with a 6-channel setup, suitable for unobtrusive headbands, and rare false detections in controls (0.03%) and young adults (0.02%).

Conclusions:

  • The proposed EEG phase synchronization-based detector effectively identifies absence seizures and quantifies seizure fragmentation.
  • The detector's ability to analyze real-time data and its performance with a reduced channel setup offer potential for practical clinical applications.
  • Quantifying seizure fragmentation may aid in distinguishing between CAE and JAE, warranting further investigation into seizure properties and clinical characteristics.