Atormac
briv
Neurology India
menu-bar5 Open access journal indexed with Index Medicus
  Users online: 3278  
 Home | Login 
About Editorial board Articlesmenu-bullet NSI Publicationsmenu-bullet Search Instructions Online Submission Subscribe Videos Etcetera Contact
  Navigate Here 
 »   Next article
 »   Previous article
 »   Table of Contents

 Resource Links
 »   Similar in PUBMED
 »Related articles
 »   Citation Manager
 »   Access Statistics
 »   Reader Comments
 »   Email Alert *
 »   Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed1939    
    Printed41    
    Emailed0    
    PDF Downloaded65    
    Comments [Add]    

Recommend this journal

 

 REVIEW ARTICLE
Year : 2021  |  Volume : 69  |  Issue : 3  |  Page : 560--566

Artificial Intelligence in Epilepsy


1 Department of Electrical, Engineering, IIT Delhi, New Delhi, India
2 Department of Neuroscience, AIIMS, New Delhi, India

Correspondence Address:
Dr. Tapan K Gandhi
Department of Electrical Engineering, Block II, IIT Delhi, Hauz Khas, New Delhi - 110 016
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.317233

Rights and Permissions

Background: The study of seizure patterns in electroencephalography (EEG) requires several years of intensive training. In addition, inadequate training and human error may lead to misinterpretation and incorrect diagnosis. Artificial intelligence (AI)-based automated seizure detection systems hold an exciting potential to create paradigms for proper diagnosis and interpretation. AI holds the promise to transform healthcare into a system where machines and humans can work together to provide an accurate, timely diagnosis, and treatment to the patients. Objective: This article presents a brief overview of research on the use of AI systems for pattern recognition in EEG for clinical diagnosis. Material and Methods: The article begins with the need for understanding nonstationary signals such as EEG and simplifying their complexity for accurate pattern recognition in medical diagnosis. It also explains the core concepts of AI, machine learning (ML), and deep learning (DL) methods. Results and Conclusions: In this present context of epilepsy diagnosis, AI may work in two ways; first by creating visual representations (e.g., color-coded paradigms), which allow persons with limited training to make a diagnosis. The second is by directly explaining a complete automated analysis, which of course requires more complex paradigms than the previous one. We also clarify that AI is not about replacing doctors and strongly emphasize the need for domain knowledge in building robust AI models that can work in real-time scenarios rendering good detection accuracy in a minimum amount of time.






[FULL TEXT] [PDF]*


        
Print this article     Email this article

Online since 20th March '04
Published by Wolters Kluwer - Medknow