Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

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...

Seizures: Classification

Seizures: Classification

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:

You might also read

Related Articles

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

Sort by

Same author

Swin-SHARP: a novel approach to wheat disease classification using boosted MAML and weighted ensembling with deep learning classifiers.

Scientific reports·2026

Same author

From local textures to global attention: a hybrid feature fusion network for skin lesion classification.

Scientific reports·2026

Same author

A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.

Scientific reports·2026

Same author

Correction: EgoVision: a YOLO-ViT hybrid for robust egocentric object recognition.

Scientific reports·2026

Same author

Language assisted learnable hyperdimensional computing framework for retinal disease classification.

Scientific reports·2026

Same author

Lipopolysaccharide of Klebseilla pneumoniae as a potential vaccine target: extraction, purification and structural insights.

Folia microbiologica·2025

Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026

Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026

Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

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

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

Published on: September 27, 2024

A deep learning based ensemble learning method for epileptic seizure prediction.

Syed Muhammad Usman¹, Shehzad Khalid¹, Sadaf Bashir²

¹Department of Computer Engineering, Bahria University, Islamabad, Pakistan.

Computers in Biology and Medicine

|August 7, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep learning ensemble method for predicting epileptic seizures, achieving high accuracy and specificity. The approach effectively mitigates challenges in seizure prediction for improved patient outcomes.

Keywords:

EEG Epilepsy prediction Preictal state Scalp EEG Seizures iEEG

More Related Videos

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

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

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

Published on: June 25, 2016

Related Experiment Videos

Last Updated: Oct 25, 2025

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

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

Published on: September 27, 2024

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

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

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

Published on: June 25, 2016

Area of Science:

Neurology
Biomedical Engineering
Artificial Intelligence

Background:

Over 30% of epilepsy patients have seizures uncontrolled by medication or surgery.
Accurate epileptic seizure prediction remains a significant challenge, particularly with low false positive rates.
Existing machine/deep learning methods show promise but require further refinement for clinical application.

Purpose of the Study:

To develop and evaluate a deep learning-based ensemble learning method for accurate epileptic seizure prediction.
To address the challenge of accurate seizure prediction with a low false positive rate.
To improve seizure management by enabling timely intervention before seizures occur.

Main Methods:

EEG signal preprocessing using empirical mode decomposition and bandpass filtering.
Generative adversarial networks (GANs) for synthetic preictal segment generation to address class imbalance.
A three-layer customized convolutional neural network (CNN) for automated feature extraction, combined with handcrafted features.
Ensemble classifier integrating Support Vector Machine (SVM), CNN, and Long Short-Term Memory (LSTM) outputs via Model-Agnostic Meta-Learning (MAML).

Main Results:

Achieved an average sensitivity of 96.28% and specificity of 95.65% with a 33-minute anticipation time on the CHBMIT dataset.
Attained an average sensitivity of 94.2% and specificity of 95.8% on the American Epilepsy Society-Kaggle seizure prediction dataset.
Demonstrated the effectiveness of the deep learning ensemble approach in enhancing seizure prediction accuracy and reliability.

Conclusions:

The proposed deep learning ensemble method significantly improves epileptic seizure prediction accuracy and reliability.
The integration of advanced signal processing, generative models, and ensemble learning offers a robust solution for seizure forecasting.
This approach holds potential for better clinical management of epilepsy by enabling proactive interventions.