An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term Eeg Based on Deep Learning Publisher Pubmed



Oghabian Z¹ ; Ghaderi R¹ ; Mohammadi M² ; Nikbakht S² Show Affiliations
Source: Brain Topography Published:2025

Abstract

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks especially convolutional networks, and its capability for detection of complex epileptic wave forms, inspired us to evaluate YOLO network for spike detection solution.The most used versions of YOLO (V3, V4 and V7) were evaluated for various epileptic signals. The epileptic discharge wave-forms were first labeled to 9 different signal types, but classified to four group combinations based on their features. EEG data from 20 patients were used under guidance of expert epileptologist. The YOLO networks were all trained for four various class-grouping strategies. The most suitable network to recommend was found to be YOLO-V4, for all four classifying methods giving average sensitivity, specificity, and accuracy of 96.7, 94.3, and 92.8, respectively. YOLO networks have shown promising results in detection of epileptic signals, which by adding some extra measurements this can become a great assistant tool for epileptologists. In addition, besides YOLO’s High speed and accuracy in detection of epileptic signals in EEG, it can classify these signals to different morphologies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.