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

545
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:
545
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

253
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...
253

You might also read

Related Articles

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

Sort by
Same author

Strengthening care and research in ALS in Southeast Asia: a call for action.

Amyotrophic lateral sclerosis & frontotemporal degeneration·2026
Same author

Three Low-Dose Antihypertensive Agents in a Single Pill after Intracerebral Hemorrhage.

The New England journal of medicine·2026
Same author

RNA gene expression and cognitive reserve as determinants of post-ischaemic stroke cognitive recovery.

Scientific reports·2026
Same author

Low-Intensity Monitoring for Mild-to-Moderate Acute Ischemic Stroke Is Cost Saving: Economic Evaluation for OPTIMISTmain.

Stroke·2026
Same author

Late Window Imaging Selection for Endovascular Therapy of Large Vessel Occlusion Stroke: An International Survey.

Stroke (Hoboken, N.J.)·2026
Same author

Exploring the Effects of Palm Tocotrienol-Rich Fraction in Diabetic Peripheral Neuropathy Rat's Model: An Untargeted Metabolomic Profiling and Correlation Study.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Aug 25, 2025

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

15.4K

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.

Tahereh Najafi1, Rosmina Jaafar1, Rabani Remli2

  • 1Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary

This study introduces a novel epilepsy detection model using electroencephalography (EEG) signals. The algorithm accurately distinguishes between normal and epileptic individuals, and classifies focal and generalized epilepsy with high performance.

Keywords:
classificationelectroencephalography (EEG)epilepsylong short-term memory (LSTM)longitudinal bipolar montage (LB)signal processingtheta frequency band

More Related Videos

Manipulation of Epileptiform Electrocorticograms ECoGs and Sleep in Rats and Mice by Acupuncture
09:06

Manipulation of Epileptiform Electrocorticograms ECoGs and Sleep in Rats and Mice by Acupuncture

Published on: December 22, 2016

9.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Related Experiment Videos

Last Updated: Aug 25, 2025

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

15.4K
Manipulation of Epileptiform Electrocorticograms ECoGs and Sleep in Rats and Mice by Acupuncture
09:06

Manipulation of Epileptiform Electrocorticograms ECoGs and Sleep in Rats and Mice by Acupuncture

Published on: December 22, 2016

9.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Area of Science:

  • Neurology
  • Medical Diagnostics
  • Signal Processing

Background:

  • Epilepsy is a neurological disorder diagnosed via electroencephalography (EEG) analysis.
  • Surgical intervention is the sole treatment for refractory epilepsy, necessitating accurate classification of epilepsy types.

Purpose of the Study:

  • To develop an automated model for diagnosing epilepsy and classifying focal versus generalized epilepsy syndromes.
  • To improve diagnostic accuracy and efficiency in epilepsy management.

Main Methods:

  • A classification model integrating longitudinal bipolar montage (LB), discrete wavelet transform (DWT), and feature extraction was employed.
  • Recurrent neural networks (RNN) with long short-term memory (LSTM) were utilized for signal classification after feature selection.
  • EEG signals were decomposed, and 15 features were extracted and fed into the LSTM model.

Main Results:

  • The model achieved 96.1% accuracy, 96.8% sensitivity, and 97.4% specificity in differentiating normal from epileptic subjects.
  • The algorithm successfully diagnosed focal and generalized epilepsy using statistical parameters on analyzed channels.

Conclusions:

  • The proposed method demonstrates significant promise for accurate epilepsy detection.
  • The approach offers satisfactory classification performance for diagnosing epilepsy and its subtypes.