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

2.1K
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:
2.1K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Protective Effect of Irisin on Atherosclerosis via Suppressing Oxidized Low Density Lipoprotein Induced Vascular Inflammation and Endothelial Dysfunction.

PloS one·2016
Same author

Comparison of affective and semantic priming in different SOA.

Cognitive processing·2016
Same author

miR-153-3p, a new bio-target, is involved in the pathogenesis of acute graft-versus-host disease via inhibition of indoleamine- 2,3-dioxygenase.

Oncotarget·2016
Same author

A novel technique for the contrast-enhanced microCT imaging of murine intervertebral discs.

Journal of the mechanical behavior of biomedical materials·2016
Same author

Pseudo isobaric peptide termini labelling for relative proteome quantification by SWATH MS acquisition.

The Analyst·2016
Same author

The influence of childhood welfare participation on adulthood substance use: evidence from the National Longitudinal Study of Adolescent to Adult Health.

The American journal of drug and alcohol abuse·2016

Related Experiment Video

Updated: Mar 25, 2026

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

3.4K

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

Shasha Yuan1,2, Weidong Zhou1,2, Qi Wu1,2

  • 11 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.

International Journal of Neural Systems
|February 25, 2016
PubMed
Summary

A new algorithm uses log-Euclidean Gaussian kernel sparse representation on electroencephalography (EEG) data for accurate epileptic seizure detection. This method efficiently processes multi-channel EEG, improving upon traditional techniques.

Keywords:
EEGlog-Euclidean Gaussian kernelseizure detectionsparse representationsymmetric positive definite matrix

More Related Videos

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

13.1K
Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

27.3K

Related Experiment Videos

Last Updated: Mar 25, 2026

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

3.4K
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

13.1K
Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

27.3K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epileptic seizure detection is crucial for diagnosis and reducing manual electroencephalography (EEG) review workload.
  • Traditional sparse representation (SR) methods are limited to Euclidean vector spaces, not ideal for complex EEG data.
  • EEG data analysis often involves complex, non-linear structures requiring advanced mathematical frameworks.

Purpose of the Study:

  • To develop a novel algorithm for accurate and efficient epileptic seizure detection from long-term EEG recordings.
  • To adapt sparse representation techniques for analyzing EEG data within the non-linear space of symmetric positive definite (SPD) matrices.
  • To improve the speed and efficiency of seizure detection by handling multi-channel EEG data synchronously.

Main Methods:

  • Employed a log-Euclidean Gaussian kernel-based sparse representation (SR) framework operating on SPD matrices derived from EEG epochs.
  • Utilized covariance descriptors to generate SPD matrices representing EEG signal segments.
  • Embedded the Riemannian manifold of SPD matrices into a reproducing kernel Hilbert space (RKHS) for SR analysis.
  • Classified seizures by computing minimal reconstructed residuals after sparse coding EEG epochs against a dictionary of training samples.

Main Results:

  • The proposed log-Euclidean Gaussian kernel-based SR method demonstrated notable performance in epoch-based and event-based seizure detection assessments.
  • The algorithm was evaluated on the Freiburg EEG dataset, comprising recordings from 21 patients.
  • The method successfully handled multiple EEG channels synchronously, offering enhanced speed and efficiency compared to conventional approaches.

Conclusions:

  • The novel log-Euclidean Gaussian kernel-based SR approach provides an effective and efficient method for epileptic seizure detection in long-term EEG recordings.
  • This technique advances SR applications by successfully adapting them to the non-linear geometry of SPD matrices inherent in EEG data.
  • The synchronous multi-channel processing capability offers a significant improvement in speed and efficiency for clinical EEG analysis.