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

Seizures: Classification01:13

Seizures: Classification

871
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:
871
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

686
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...
686

You might also read

Related Articles

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

Sort by
Same author

Methodological Insights Into the Acceleration-Speed Profile: Optimizing Data Analysis for Reliable Application in Elite Female and Male Football.

International journal of sports physiology and performance·2025
Same journal

Anterior Cingulate Cortex Mediates State-Dependent Prioritization of Distressed Conspecifics.

Brain sciences·2026
Same journal

Hemispherotomy for Pediatric Post-Traumatic Epilepsy.

Brain sciences·2026
Same journal

When Robots Learn: Artificial Intelligence and the Next Human-Centered Era of Neurorehabilitation.

Brain sciences·2026
Same journal

The Association Between Changes in White Matter Microstructure and Cognitive Function in Older Adults with Mild Cognitive Impairment.

Brain sciences·2026
Same journal

Beyond Ventricular Enlargement: Multimodal MRI Assessment Improves Surgical Decision-Making in Normal Pressure Hydrocephalus.

Brain sciences·2026
Same journal

The Effects of Personalized Observation, Execution, and Mental Imagery (POEM) Therapy in Logopenic Primary Progressive Aphasia: A Telepractice-Based Single-Case Study.

Brain sciences·2026
See all related articles

Related Experiment Video

Updated: Nov 7, 2025

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

3.0K

EEG-Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction.

James Brian Romaine1, Mario Pereira Martín1, José Ramón Salvador Ortiz1

  • 1Departamento Ingenería, Universidad Loyola Andalucía, Dos Hermanas, 41704 Seville, Spain.

Brain Sciences
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a simplified method for detecting epileptic seizures using a single electroencephalography channel. The novel approach minimizes calculations while achieving high accuracy, suggesting potential for seizure prediction.

Keywords:
Alzheimer diseaseDSPParkinsons diseasedetectionenvelopeepilepsyhilbert transformpredictionsynchronisation

More Related Videos

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

20.7K
Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

26.2K

Related Experiment Videos

Last Updated: Nov 7, 2025

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

3.0K
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

20.7K
Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

26.2K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epileptic seizure detection and classification are critical for patient care.
  • Existing multi-channel methods are computationally intensive.
  • There is a need for efficient and accurate seizure detection algorithms.

Purpose of the Study:

  • To develop a computationally efficient method for detecting and classifying epileptic seizures.
  • To reduce the complexity of electroencephalography (EEG) analysis for seizure detection.
  • To explore the potential for seizure prediction based on pre-ictal synchronization variations.

Main Methods:

  • Calculating instantaneous phase between upper and lower envelopes of a single EEG channel.
  • Minimizing computational workload by reducing electrode combinations.
  • Simulating over 600 hours of data for validation.

Main Results:

  • Achieved 100% sensitivity and specificity for high false-positive rates.
  • Demonstrated 83% sensitivity and 75% specificity for moderate to low false-positive rates.
  • Detected pre-ictal synchronization variations in over 90% of patients.

Conclusions:

  • The proposed single-channel method is highly accurate and computationally efficient for epileptic seizure detection.
  • The method shows comparable or superior performance to existing single- and multi-channel approaches.
  • Pre-ictal synchronization analysis indicates potential for developing a seizure prediction system.