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 ll: Types01:22

Epilepsy ll: Types

47
Recurrent seizures, stemming from abnormal electrical activity in the brain, are the defining characteristic of epilepsy, a chronic neurological condition. Because seizure features vary greatly, epilepsy is classified using two systems: by seizure type and by epilepsy syndromes. These classifications enable clinicians to describe seizure patterns and select suitable treatment strategies.I. Classification by Seizure Type1. Focal EpilepsyFocal epilepsy begins in one hemisphere of the brain.
47
Seizures: Classification01:13

Seizures: Classification

2.5K
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:
2.5K
Seizures l: Introduction01:20

Seizures l: Introduction

42
Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...
42
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.7K
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...
1.7K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Seizures ll: Types01:19

Seizures ll: Types

36
Seizures are sudden bursts of abnormal electrical discharge in the brain that interfere with normal function. They are commonly divided into three groups: focal seizures, generalized seizures, and other types that do not fit neatly into either category.Focal SeizuresFocal seizures begin in a single brain region. When awareness is preserved, they are called focal aware seizures and may cause sensations such as tingling, unusual smells, or flashing lights. When awareness is impaired, they are...
36

You might also read

Related Articles

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

Sort by
Same author

Perineuronal net abnormalities in epileptic human tissue.

Epilepsia·2026
Same author

Structural features of the hippocampus covary with memory-guided attention depending on the side of hippocampal sclerosis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Slow-Wave Sleep Fragmentation in Disorders of Arousal and Sleep-Related Hypermotor Epilepsy: A Multicenter Polysomnographic Study.

Neurology·2026
Same author

Pediatric epilepsy surgery: Global survey of invasive explorations.

Epilepsia·2026
Same author

Microbiota-gut-brain axis and treatment resistance in epilepsy: a multicentre prospective study protocol (CARE).

BMJ open·2026
Same author

High-intensity focused ultrasound (HIFU) modeling: in vitro validation and integration into patient-specific planning tool.

Scientific reports·2026
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

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

13.7K

Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.

Yohannes Kassahun1, Roberta Perrone2, Elena De Momi2

  • 1Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany.

Artificial Intelligence in Medicine
|April 19, 2014
PubMed
Summary
This summary is machine-generated.

This study developed machine learning methods for automatic epilepsy classification, achieving performance comparable to experienced clinicians in identifying seizure origins and types.

Keywords:
Data mining (knowledge discovery) from medical dataEpileptogenic zone identificationGenetics-based classificationOntology-based classification

More Related Videos

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

2.7K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.5K

Related Experiment Videos

Last Updated: May 1, 2026

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

13.7K
Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

2.7K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.5K

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Accurate identification of the epileptogenic zone is crucial for presurgical planning in drug-resistant focal epilepsies.
  • Current methods rely on manual analysis of clinical, electrophysiological, and neuroimaging data.
  • Clinical evaluation often involves subjective visual detection of seizure symptoms.

Purpose of the Study:

  • To develop and evaluate fully automatic classifiers for epilepsy types and localization.
  • To utilize ictal symptoms and machine learning for epilepsy diagnosis.
  • To compare the performance of automated methods against human expert clinicians.

Main Methods:

  • Employed two machine learning approaches: ontology-based classification and genetics-based data mining.
  • Tested methods on a clinical dataset of 129 patients with focal epilepsy.
  • Compared algorithm performance against seven expert clinicians classifying temporal lobe epilepsy and extra-temporal lobe epilepsy.

Main Results:

  • Automated methods demonstrated performance comparable to, and slightly exceeding, individual clinicians on randomly selected test sets.
  • Accuracy for the methods ranged from 65.6% to 77.8%, compared to clinicians' 56.3% to 77.8%.
  • Overall mean accuracy for the methods (60%) was slightly lower than clinicians (61.6%) when evaluated on clinician-selected cases.

Conclusions:

  • The developed machine learning methods show significant potential for fully automatic epilepsy classification.
  • These methods can aid in identifying key diagnostic signs for epilepsy.
  • Automated classification performs at the level of experienced clinicians when using identical information.