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

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

Risk stratification in diabetic ankle fractures: a systematic review of reviews and proposal of the MADRAS scoring system.

Cardiovascular diabetology. Endocrinology reports·2026

Same author

Microbial fermentation of cereals: nutritional, techno-functional, and compositional transformations toward data-driven process optimization.

Critical reviews in food science and nutrition·2026

Same author

A Cost-Effective 3D-Printed Conductive Phantom for EEG Sensing System Validation: Development, Performance Evaluation, and Comparison with State-of-the-Art Technologies.

Sensors (Basel, Switzerland)·2025

Same author

From Stool to Scope: Optimising FIT Thresholds to Guide Future Panenteric Capsule Endoscopy and Reduce Colonoscopy Burden in Iron Deficiency Anaemia.

Cancers·2025

Same author

Fabrication of highly tough, self-healing sodium alginate/polyacrylamide and copper based nanocomposite hydrogel and its application as strain and pressure sensor for human health monitoring and signature recognition.

International journal of biological macromolecules·2025

Same author

A highly stretchable, self-healing, self-adhesive polyacrylic acid/chitosan multifunctional composite hydrogel for flexible strain sensors.

Carbohydrate polymers·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

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

An Attention-Residual Convolutional Network for Real-Time Seizure Classification on Edge Devices.

Peter A Akor¹, Godwin Enemali¹, Usman Muhammad¹

¹School of Science and Engineering, Glasgow Caledonian University, Glasgow G4 0BA, UK.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

A new AI model, EEG-ARCNet, accurately classifies epilepsy seizure types from EEG data. This efficient deep learning tool shows promise for real-time seizure monitoring, even on low-power devices.

Keywords:

EEG analysis Raspberry Pi attention mechanism edge computing epilepsy monitoring residual network seizure classification

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

Related Experiment Videos

Last Updated: Jan 10, 2026

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

Area of Science:

* Neuroscience and Artificial Intelligence
* Medical Signal Processing
* Machine Learning for Healthcare

Background:

* Epilepsy impacts over 50 million globally, necessitating precise seizure type classification for effective treatment.
* Manual electroencephalogram (EEG) interpretation is time-consuming and requires expert knowledge, hindering clinical workflows.
* Accurate seizure classification is crucial as different types require specific antiepileptic drugs.

Purpose of the Study:

* To develop and evaluate EEG-ARCNet, an attention-residual convolutional network for automated multi-channel EEG seizure classification.
* To assess the model's performance in distinguishing between five common seizure types.
* To validate the feasibility of deploying EEG-ARCNet on edge devices for practical seizure monitoring.

Main Methods:

* Developed EEG-ARCNet, integrating residual connections and channel attention for temporal and spectral EEG feature extraction.
* Combined nine statistical temporal features with five frequency-band power measures using Welch's spectral decomposition.
* Evaluated the model on the Temple University Hospital Seizure Corpus, comprising multi-channel EEG recordings.

Main Results:

* Achieved high classification accuracy (99.65%) and macro-averaged F1-score (99.59%) across five seizure types.
* Demonstrated efficient edge deployment on a Raspberry Pi 4 with a 2.06 ms inference time per 10s segment.
* Reported low resource utilization: 35.4% CPU and 499.4 MB memory consumption.

Conclusions:

* EEG-ARCNet offers a highly accurate and efficient solution for automated epilepsy seizure classification.
* The model's performance and low resource requirements support its use in resource-constrained seizure-monitoring applications.
* This technology holds potential for improving clinical workflows and patient outcomes in epilepsy management.