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

Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.5K
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...
1.5K
Seizures: Classification01:13

Seizures: Classification

1.8K
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:
1.8K

You might also read

Related Articles

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

Sort by
Same author

Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.

Foods (Basel, Switzerland)·2022
Same author

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

Computer methods and programs in biomedicine·2022
Same author

Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort.

Computer methods and programs in biomedicine·2019
Same author

A novel edge based embedding in medical images based on unique key generated using sudoku puzzle design.

SpringerPlus·2016

Related Experiment Video

Updated: Feb 25, 2026

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

27.2K

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm.

B Suguna Nanthini1, B Santhi1

  • 1ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.

Journal of Natural Science, Biology, and Medicine
|August 8, 2017
PubMed
Summary

This study introduces an automated seizure detection model using electroencephalogram (EEG) signals. Feature selection via genetic algorithm (GA) significantly improved the accuracy of seizure detection with a support vector machine (SVM) classifier.

Keywords:
Accuracyclassificationepilepsygenetic algorithmseizuresignal

More Related Videos

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

21.2K
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

13.0K

Related Experiment Videos

Last Updated: Feb 25, 2026

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

27.2K
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

21.2K
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

13.0K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epilepsy is a neurological disorder characterized by recurrent seizures.
  • Electroencephalogram (EEG) testing is crucial for diagnosing brain disorders, particularly epilepsy.
  • Automated seizure detection models offer a promising approach for improved diagnosis and management.

Purpose of the Study:

  • To develop and validate an automated model for detecting seizures from EEG signals.
  • To investigate the efficacy of feature selection techniques in enhancing seizure detection accuracy.
  • To evaluate the performance of a support vector machine (SVM) classifier for automated epilepsy diagnosis.

Main Methods:

  • EEG signals were decomposed into sub-bands using discrete wavelet transform (db2 wavelet).
  • Feature extraction included statistical features, gray level co-occurrence matrix, and Renyi entropy.
  • Genetic algorithm (GA) was employed for optimal feature selection, reducing dimensions from 16 to 8.
  • A support vector machine (SVM) classifier was trained and tested on selected features.

Main Results:

  • The automated seizure detection model achieved satisfactory performance across two different EEG databases.
  • The selected relevant features significantly contributed to the model's accuracy.
  • The SVM classifier demonstrated effective classification of EEG signals for seizure detection.

Conclusions:

  • Feature selection using genetic algorithm (GA) enhances the accuracy of automated seizure detection.
  • The developed model shows potential for clinical application in epilepsy diagnosis.
  • Optimized feature sets are critical for robust and reliable seizure detection systems.