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

1.4K
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:
1.4K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

A new approach based on the median filter to T-wave detection in ECG signal.

Journal of medical engineering & technology·2014
Same author

Automatic classification of heartbeats using wavelet neural network.

Journal of medical systems·2010
See all related articles

Related Experiment Video

Updated: Jan 19, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

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

Published on: December 6, 2016

21.1K

Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with

Dib Nabil1, Radhwane Benali1, Fethi Bereksi Reguig1

  • 1Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, Tlemcen 13048, Algeria.

Biomedizinische Technik. Biomedical Engineering
|September 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for detecting epileptic seizures (ES) using short electroencephalogram (EEG) recordings. The approach achieves high accuracy, comparable to existing state-of-the-art techniques, offering a more efficient alternative to manual analysis.

Keywords:
Lyapunov exponentsapproximate entropydiscrete wavelet transformepileptic seizureshigher order spectraphase entropysupport vector machine

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

12.9K
Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury
07:07

Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury

Published on: February 10, 2020

11.2K

Related Experiment Videos

Last Updated: Jan 19, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

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

Published on: December 6, 2016

21.1K
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

12.9K
Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury
07:07

Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury

Published on: February 10, 2020

11.2K

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epileptic seizure (ES) detection from electroencephalogram (EEG) signals is crucial for neurological disorder management.
  • Manual EEG analysis for ES detection is labor-intensive, time-consuming, and costly.
  • Automated ES recognition from EEG is essential for efficient and accurate diagnosis.

Purpose of the Study:

  • To propose a novel method for automatic epileptic seizure recognition using short-term EEG recordings.
  • To enhance the efficiency and accuracy of ES detection compared to traditional methods.
  • To validate the proposed method using a standard EEG database and benchmark against existing research.

Main Methods:

  • Decomposition of EEG signals into sub-signals using discrete wavelet transform.
  • Extraction of non-linear parameters (approximate entropy, largest Lyapunov exponents) and statistical features from sub-signals.
  • Utilizing phase entropies from higher-order spectrum analysis as input features for a multi-class support vector machine (MSVM).

Main Results:

  • The proposed method achieved high classification accuracy (Ac), sensitivity (Se), and specificity (Sp) in ES recognition.
  • Performance metrics were comparable to the best state-of-the-art methods on the University of Bonn EEG database.
  • The method demonstrates effectiveness in automatic ES detection from short EEG segments.

Conclusions:

  • The developed automated method provides an efficient and accurate approach for epileptic seizure detection.
  • The combination of wavelet transform, non-linear parameters, and higher-order spectral analysis is effective for ES recognition.
  • This technique offers a promising alternative for clinical application in epilepsy diagnosis.