Jove
Visualize
Contact Us

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

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope.

Journal of intelligent & robotic systems·2021
See all related articles
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 Experiment Video

Updated: Dec 15, 2025

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.5K

Automated human mind reading using EEG signals for seizure detection.

Virender Ranga1, Shivam Gupta2, Jyoti Meena1

  • 1Department of Computer Engineering, National Institute of Technology, Kurukshetra, India.

Journal of Medical Engineering & Technology
|July 14, 2020
PubMed
Summary

This study introduces a deep learning model to automatically detect epilepsy patterns from electroencephalogram (EEG) data. The developed automated system achieves 98.33% accuracy, aiding neurologists in diagnosing this common neurological disorder.

Keywords:
EEGEpilepsydeep learningmedical diagnosisneural networkseizure

More Related Videos

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.2K
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

20.9K

Related Experiment Videos

Last Updated: Dec 15, 2025

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.5K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.2K
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

20.9K

Area of Science:

  • Neurology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Epilepsy, a global neurological disorder affecting 50 million people, is characterized by recurrent seizures.
  • Current diagnosis relies on manual electroencephalogram (EEG) interpretation by neurologists, which is time-consuming and requires extensive expertise.
  • Advancements in medical science necessitate automated solutions to improve diagnostic efficiency and accuracy.

Purpose of the Study:

  • To develop an automated system for detecting and classifying epilepsy patterns using deep learning.
  • To assist neurologists in diagnosing epilepsy by providing accurate and efficient seizure detection.
  • To enhance the performance and reduce the workload of neurosurgeons.

Main Methods:

  • Utilized deep learning, specifically neural networks, for analyzing electroencephalogram (EEG) data.
  • Developed a novel model for automated seizure region detection and classification.
  • Trained and validated the model on a dataset to assess its performance.

Main Results:

  • The proposed model achieved a diagnostic accuracy of 98.33%.
  • Demonstrated the potential of deep learning in automating the analysis of complex neurological data.
  • Indicated significant improvement over manual interpretation in terms of accuracy and efficiency.

Conclusions:

  • The developed automated system shows high accuracy in detecting epilepsy patterns from EEG.
  • Deep learning models offer a promising approach to support clinical decision-making in neurology.
  • This technology can significantly aid neurologists, improving patient care and diagnostic outcomes.