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

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:
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

You might also read

Related Articles

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

Sort by
Same author

Wearable Camera-Based Dietary Assessment of Mother-Father Dyads in Urban and Rural Households in Ghana.

Current developments in nutrition·2026
Same author

Eating architecture components and their associations with BMI in urban and rural Ghanaian mothers, fathers, children, and adolescents, assessed using a wearable camera: A cross-sectional study.

Chronobiology international·2026
Same author

Mechanisms of Generative Image-to-Image Translation Networks.

IEEE access : practical innovations, open solutions·2026
Same author

Image-Based Volume Estimation for Food in a Bowl.

Journal of food engineering·2026
Same author

Experience of Using Wearable Devices for Dietary Management for Chinese Americans With Type 2 Diabetes: One-Group Prospective Cohort Study.

JMIR diabetes·2025
Same author

Energy and Nutrient Intakes of Public Health Concern by Rural and Urban Ghanaian Mothers Assessed by Weighed Food Compared to Recommended Intakes.

Nutrients·2025

Related Experiment Video

Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Optimal feature selection for seizure detection: a subspace based approach.

Tolga E Ozkurt1, Mingui Sun, Tayfun Akgul

  • 1Dept. of Neurological Surg., Pittsburgh Univ., PA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a new EEG feature, sAMDF, for epilepsy detection. A divergence-based subspace method effectively selects features, improving seizure discrimination from non-seizure signals.

Related Experiment Videos

Last Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neurology

Background:

  • Epileptic seizure detection relies heavily on effective feature extraction and selection from electroencephalogram (EEG) data.
  • Existing methods may face challenges in computational efficiency and discriminative power.

Purpose of the Study:

  • To introduce the short-time average magnitude difference function (sAMDF) as a computationally efficient EEG feature for seizure detection.
  • To compare sAMDF with the curve length feature.
  • To propose and evaluate a subspace-based feature selection approach using a divergence measure.

Main Methods:

  • The short-time average magnitude difference function (sAMDF) was computed from EEG signals.
  • sAMDF and curve length features were compared for seizure discrimination.
  • A subspace-based feature selection method utilizing a divergence measure was applied.
  • Features were linearly transformed into a reduced space to optimize performance and reduce computational load.

Main Results:

  • The proposed sAMDF feature demonstrated effectiveness in distinguishing seizures from non-seizure EEG.
  • The divergence-based subspace approach for feature selection proved beneficial.
  • Transformed features showed comparable or improved seizure discrimination performance compared to original features.

Conclusions:

  • The sAMDF is a computationally efficient and effective feature for EEG-based epilepsy detection.
  • Feature selection using a divergence-based subspace approach significantly enhances the ability to discriminate seizure events from background EEG activity.