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

You might also read

Related Articles

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

Sort by
Same author

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema.

BMC medical imaging·2024
Same author

Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

Frontiers in neuroinformatics·2024
Same author

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2022
Same author

Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2021
Same author

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Physical and engineering sciences in medicine·2020
Same author

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus.

Evidence-based complementary and alternative medicine : eCAM·2020

Related Experiment Video

Updated: Jul 4, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

38.8K

Advanced framework for epilepsy detection through image-based EEG signal analysis.

Palani Thanaraj Krishnan1, Sudheer Kumar Erramchetty1, Bhanu Chander Balusa1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in Human Neuroscience
|February 6, 2024
PubMed
Summary

This study introduces an image-based machine learning method for epilepsy detection using Gramian Angular Summation Fields (GASF) and feature extraction techniques like SIFT and ORB, achieving high accuracy in classifying EEG signals.

Keywords:
EEG signal processingGramian angular summation fieldepilepsyimage-based feature extractionmachine learning classifiersoriented FAST and rotated BRIEFscale invariant feature transform

More Related Videos

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

Related Experiment Videos

Last Updated: Jul 4, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

38.8K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Epilepsy is a neurological disorder characterized by recurrent seizures, necessitating accurate diagnosis for effective treatment.
  • Electroencephalography (EEG) time-series analysis is vital for epilepsy diagnosis but traditional methods are computationally intensive.
  • Machine learning offers potential for improved epilepsy detection, focusing on advanced feature extraction from EEG data.

Purpose of the Study:

  • To investigate the efficacy of Gramian Angular Summation Field (GASF) for transforming EEG signals into images.
  • To explore the use of Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) for extracting image features from EEG data for epilepsy detection.
  • To evaluate the performance of machine learning classifiers in distinguishing normal and focal EEG patterns using these image-based features.

Main Methods:

  • EEG signals were converted into images using the GASF approach.
  • SIFT and ORB techniques were applied to extract relevant features from the GASF images.
  • A Random Forest classifier was employed to differentiate between normal and focal EEG patterns, with performance validated against SVM and k-NN.

Main Results:

  • The proposed method achieved high classification accuracy: 96% with SIFT features and 94% with ORB features.
  • The Random Forest classifier demonstrated superior performance in precision, recall, F1-score, specificity, and Area Under Curve (AUC) compared to other classifiers.
  • Receiver Operating Characteristic (ROC) curve analysis confirmed the superiority of Random Forest over Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN).

Conclusions:

  • The novel image-based preprocessing pipeline using GASF, SIFT, and ORB offers significant advantages over traditional time-series EEG analysis.
  • This method accurately discriminates between normal and focal EEG signals, paving the way for earlier and more precise epilepsy diagnosis.
  • The findings suggest improved patient outcomes through enhanced early detection and management of epilepsy.