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

1.7K
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:
1.7K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.4K
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...
1.4K

You might also read

Related Articles

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

Sort by
Same author

First draft genome and genetic diversity analysis of the economically important and vulnerable tree species Saraca asoca (Roxb.) W.J. de Wilde.

Journal of genetics·2026
Same author

Ubiquitous Neural Cell Adhesion Molecule (NCAM): Potential Mechanism and Valorisation in Cancer Pathophysiology, Drug Targeting and Molecular Transductions.

Molecular neurobiology·2022
Same author

A case report of extensive cerebral venous sinus thrombosis: a sequela of COVID-19?

Acute medicine·2021
Same author

Hybrid control approaches for hands-free high level human-computer interface-a review.

Journal of medical engineering & technology·2020
Same author

Bio-augmentation of heterotrophic bacteria in biofloc system improves growth, survival, and immunity of Indian white shrimp Penaeus indicus.

Fish & shellfish immunology·2020
Same author

Blood brain barrier: A tissue engineered microfluidic chip.

Journal of neuroscience methods·2019

Related Experiment Video

Updated: Feb 16, 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

3.3K

Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case

A Sharmila1, Suman Aman Raj1, Pandey Shashank1

  • 1a School of Electrical Engineering , VIT University , Vellore , India.

Journal of Medical Engineering & Technology
|December 19, 2017
PubMed
Summary

This study introduces a novel method using discrete wavelet transform (DWT) and entropy analysis for accurate electroencephalographic (EEG) signal classification. The technique achieved 100% accuracy in distinguishing epileptic seizures from normal brain activity.

Keywords:
Entropydiscrete wavelet transformepilepsysupport vector machine

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K

Related Experiment Videos

Last Updated: Feb 16, 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

3.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K

Area of Science:

  • Signal Processing
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Electroencephalographic (EEG) signals are crucial for understanding brain activity.
  • Analyzing EEG signals for neurological disorders like epilepsy presents significant challenges due to noise and complexity.
  • Existing methods often struggle with accuracy and distortion when analyzing weak or complex EEG patterns.

Purpose of the Study:

  • To develop and evaluate a novel time-frequency domain analysis method for enhanced EEG signal classification.
  • To assess the efficacy of discrete wavelet transform (DWT) combined with Shannon entropy and approximate entropy (ApEn) for detecting epileptic activity.
  • To improve the accuracy and reliability of automated seizure detection systems.

Main Methods:

  • Utilized discrete wavelet transform (DWT) for decomposing EEG signals into multiple sub-bands (D1-D5, A5).
  • Extracted non-linear features, specifically Shannon entropy and approximate entropy (ApEn), from the decomposed EEG sub-bands.
  • Employed support vector machine (SVM) classifiers for the classification of epileptic and healthy EEG signals.

Main Results:

  • The DWT technique effectively decomposed EEG signals, enabling detailed analysis.
  • Approximate entropy (ApEn) proved to be a suitable feature for characterizing EEG, showing a distinct drop during epileptic activity.
  • The proposed classification scheme achieved high accuracy, reaching 100% in two out of fifteen tested classification problems.

Conclusions:

  • The combination of DWT, entropy measures (Shannon and ApEn), and SVM classifiers offers a robust and accurate method for EEG signal analysis.
  • This approach demonstrates significant potential for reliable detection and classification of epileptic seizures from EEG data.
  • The study highlights the effectiveness of time-frequency analysis and non-linear feature extraction in biomedical signal processing.