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

Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

376
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
376
Seizures: Classification01:13

Seizures: Classification

696
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
696

You might also read

Related Articles

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

Sort by
Same author

Complementary predictive value of electromagnetic source imaging and hemodynamic responses in epilepsy surgery: A quantitative spatial analysis.

Epilepsia·2026
Same author

[Quantification of Antioxidants in Farmed Salmon Using GC-APCI-MS/MS].

Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan·2026
Same author

GH responsiveness to corticotropin-releasing hormone identifies corticotroph-like somatotroph adenomas in acromegaly.

Pituitary·2026
Same author

Longitudinal changes in the antibody responses and adverse reactions across successive SARS-CoV-2 mRNA vaccinations from the primary series to the JN.1-adapted booster in hospital employees: A 4-year prospective cohort study.

Vaccine·2026
Same author

Corticosteroids modulate bovine oocyte maturation through mineralocorticoid receptor.

General and comparative endocrinology·2026
Same author

Hitting the Sulcus With a More Tangential Angle May Result in Resistance Felt During Implantation of Stereotactic Electroencephalography Electrodes.

Neurosurgery practice·2026

Related Experiment Video

Updated: Oct 21, 2025

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

19.5K

Data-driven electrophysiological feature based on deep learning to detect epileptic seizures.

Shota Yamamoto1,2,3, Takufumi Yanagisawa1,2,3, Ryohei Fukuma1,2

  • 1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka 567-0872, Japan.

Journal of Neural Engineering
|September 3, 2021
PubMed
Summary

Researchers developed a new electrophysiological feature, the data-driven epileptogenicity index (d-EI), to accurately detect epileptic seizures using intracranial electroencephalogram (iEEG) data. This novel index shows promise in identifying the epileptogenic zone, potentially improving surgical outcomes for epilepsy patients.

Keywords:
Epi-Netdata-driven epileptogenicity indexepilepsymodified integrated gradients

More Related Videos

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.9K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.5K

Related Experiment Videos

Last Updated: Oct 21, 2025

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

19.5K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.9K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.5K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Technology

Background:

  • Epilepsy diagnosis relies on identifying characteristic electrophysiological patterns.
  • Current methods for detecting epileptic seizures from intracranial electroencephalogram (iEEG) data can be improved for accuracy and localization.
  • Identifying the epileptogenic zone is crucial for successful surgical intervention in refractory epilepsy.

Purpose of the Study:

  • To develop and validate a novel electrophysiological feature for detecting epileptic seizures across various epilepsy types.
  • To compare the diagnostic accuracy of the new feature against conventional methods.
  • To assess the utility of the new feature in localizing the epileptogenic zone and predicting surgical outcomes.

Main Methods:

  • Intracranial electroencephalogram (iEEG) data from 21 patients with refractory epilepsy were analyzed.
  • A convolutional neural network (Epi-Net) was trained to classify seizure and interictal iEEG signals.
  • A data-driven epileptogenicity index (d-EI) was derived from Epi-Net's learned features, quantifying frequency power changes.
  • The d-EI's accuracy in seizure detection was compared with Support Vector Machine (SVM) using spectral power and phase-amplitude coupling.

Main Results:

  • Epi-Net achieved high accuracy in seizure detection (AUC = 0.944 ± 0.067), outperforming SVM (AUC = 0.808 ± 0.253).
  • The learned iEEG features involved increased power in the 17-92 Hz and >180 Hz bands and decreased power in other frequencies.
  • The derived d-EI demonstrated superior accuracy in detecting epileptic seizures compared to conventional iEEG features.
  • Higher d-EI values correlated significantly with successful surgical resection in patients with favorable Engel classifications (≤1).

Conclusions:

  • The novel data-driven epileptogenicity index (d-EI) derived from Epi-Net effectively identifies epileptic seizures with high accuracy.
  • The d-EI shows significant association with surgical resection areas and predicts seizure outcome, aiding in epileptogenic zone identification.
  • This new electrophysiological feature holds potential for improving the diagnosis and surgical planning of epilepsy.