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:

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Basic signals of Fourier Transform

Basic signals of Fourier Transform

The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...

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

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

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

A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection.

Saiby Madan¹, Kajri Srivastava¹, A Sharmila¹

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

Journal of Medical Engineering & Technology

|December 1, 2017

Summary

This study introduces Hurst exponent (HE)-based discrete wavelet transform for epilepsy detection from EEG signals. The method achieved 99% accuracy using Support Vector Machines (SVM) for classification.

Keywords:

Epilepsy Hurst exponent K-nearest neighbour discrete wavelet transform support vector machine

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Related Experiment Videos

Last Updated: Feb 17, 2026

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

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Epileptic seizures necessitate accurate diagnosis through electroencephalogram (EEG) analysis.
Conventional EEG analysis methods struggle with non-stationary signals, limiting diagnostic accuracy.
Identifying epileptic discharges is crucial for understanding epilepsy mechanisms.

Purpose of the Study:

To develop an effective method for epilepsy determination using EEG data.
To explore advanced signal processing techniques for improved feature extraction from EEG.
To classify EEG signals accurately for epilepsy diagnosis.

Main Methods:

Implementation of Hurst exponent (HE)-based discrete wavelet transform for feature extraction.
Utilizing EEG datasets from ictal and pre-ictal stages.
Classification of EEG signals using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) classifiers.

Main Results:

The HE-based discrete wavelet transform effectively extracted features from EEG signals.
SVM and KNN classifiers were employed for signal classification.
The highest classification accuracy achieved was 99% using SVM.

Conclusions:

The proposed HE-based discrete wavelet transform technique shows high efficacy in epilepsy detection.
SVM classifier demonstrates superior performance in classifying EEG signals for epilepsy diagnosis.
This approach offers a promising tool for improving the accuracy of epilepsy diagnosis.