Template-Type: ReDIF-Article 1.0
Author-Name:Arslan Muzammil, Rabia Tehseen, Maham Mehr Awan, Rubab Javaid, Anum Mustaqeem, Madiha Yousaf
Author-Email:rabia.tehseen@ucp.edu.pk
Author-Workplace-Name:Faculty of Information Technology, University of Central Punjab Lahore, Pakistan
Title:Hybrid Deep Learning Approach for EEG-based Epilepsy Detection
Abstract:Epilepsy  is  a  chronic  neurological  disorder  characterized  by  continuous  relentless seizures resulting from abnormal activity in the brain. Early and accurate diagnosis is very critical. The usual methods can take a lot of time for diagnosis and it can also often vary from one specialist to another. There have been many approaches implemented for detecting seizures with varying success. Electroencephalogram (EEG) analysis is a critical tool for diagnosing neurological conditions like epilepsy. A key focus in medical technology has been automating the detection of epilepsy but it has been challenging due to its complexity and large amount of data. Although the results of some studies have been encouraging, the use of these approaches has not been practical due to various issues i.e. imbalanced data signal variability to name a few.This research presents a new approach to improve performance and accuracy.  A  Hybrid  Deep  Learning  model  combines  a  number  of  paradigms  of  neural networks to leverage the best of multiple models in processing complex data like EEG signals. EEG. As EEG has both temporal and spatial data this hybrid approach is quite practical in handling  different  EEG  components.In  addition,  a  multimodal  method  is  explored  to enhance  prediction  performance.  This  involves  enhancing  EEG  data  with  complementary data,  such  as  clinical  history  and  other  biomarkers.  Through  integrating  data  from  multiple sources,  the  model  gains  a  broader  context  for  epileptic  activity  detection.  Which  helps  in bypassing the inefficiencies inherent in EEG signals. This combined approach can potentially provide  stronger  and  clinically  informative  outcomes,  hence  enabling  advancements  in  the early diagnosis of epilepsy.
Keywords:Electroencephalogram, Independent Component Analysis, Principal Component Analysis,   Gated   Recurrent   Unit,   Tunable-Q   Wavelet   Transform,   Synthetic   Minority Oversampling Technique, European Data Format
Journal: International Journal of Innovations in Science and Technology
Pages:950-965
Volume:7
Issue:2
Year:2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1402/1910
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1402
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:950-965