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

Factors associated with rapid pediatric acute seizure emergency treatment: Quality Improvement in Time to Treat Status Epilepticus baseline cohort.

Epilepsia·2026

Same author

Video-based diagnostics supported by artificial intelligence as an opportunity to address the epilepsy diagnostic gap: A narrative review.

Epilepsia·2026

Same author

Evaluating multimodal autonomic correlations in pediatric epilepsy using wearable biosignals.

Epilepsia·2026

Same author

Machine Learning-Based Prediction of Early Left Ventricular Function After STEMI.

Journal of clinical medicine·2025

Same author

Current practices and trends in surgical decision-making for children with Lennox-Gastaut syndrome: A cross-sectional survey by the Pediatric Epilepsy Research Consortium.

Epilepsia open·2025

Same author

Seizure monitoring by combined diary and wearable data: A multicenter, longitudinal, observational study.

Epilepsia·2025

Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026

Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026

Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026

Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026

Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026

Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 29, 2025

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

Seizure-related differences in biosignal 24-h modulation patterns.

Solveig Vieluf^1,2, Rima El Atrache³, Sarah Cantley³

¹Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA. Solveig.Vieluf@childrens.harvard.edu.

Scientific Reports

|September 6, 2022

Summary

This summary is machine-generated.

Researchers identified a new biomarker for predicting epileptic seizures using wearable sensor data. This could improve patient monitoring and treatment adjustments by forecasting seizure risk.

More Related Videos

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

Related Experiment Videos

Last Updated: Aug 29, 2025

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

Area of Science:

Biomedical Engineering
Neuroscience
Digital Health

Background:

Epileptic seizures pose significant challenges for monitoring and treatment.
A reliable seizure likelihood biomarker is needed to personalize patient care and risk assessment.

Purpose of the Study:

To investigate differences in 24-hour physiological modulation patterns between patients with and without seizures.
To develop and validate a machine learning model for predicting seizure likelihood using wearable biosensor data.

Main Methods:

Continuous video-EEG monitoring was used to identify seizure events in enrolled patients.
Physiological data, including electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR), were collected using a biosensor.
Machine learning algorithms were employed to analyze physiological and clinical variables for seizure prediction.

Main Results:

Patients with seizures exhibited significantly lower electrodermal activity levels and amplitudes compared to those without seizures.
Heart rate levels trended lower in patients experiencing seizures.
The cross-validated machine learning model achieved an average classification accuracy of 69% (AUC-ROC: 0.75).

Conclusions:

24-hour modulation patterns of EDA, TEMP, and HR, combined with clinical data, show potential as biomarkers for monitoring and forecasting epileptic seizure risk.
These findings suggest the utility of wearable technology for informing clinical decisions, optimizing treatment, and managing seizure-related risks.
The developed biomarker may also have applications in other neurological and chronic conditions.