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CC BY-NC-ND 4.0 · International Journal of Epilepsy
DOI: 10.1055/s-0045-1806851
Original Article

Postprocessing Deep Neural Network for Performance Improvement of Interictal Epileptiform Discharge Detection

Galia V. Anguelova 
1   Department of Neurology, Haaglanden Medical Centre, The Hague, The Netherlands
2   Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Zwolle, The Netherlands
,
Patricia M. Baines
3   Department of BioMechanical Engineering, Delft University of Technology, Delft, The Netherlands
› Author Affiliations
Funding None.
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Abstract

Objective

Automated detection of interictal epileptiform discharges (IEDs) on electroencephalographic (EEG) data aims to reduce the time and resources spent on visual analysis by experts (the gold standard) with algorithms that match or outperform experts. In this study, we aimed to further improve IED detection performance of a deep neural network based algorithm with a simpler second-level postprocessing deep learning network, a new approach in this field.

Materials and Methods

Seventeen interictal ambulatory EEGs were used, 15 with focal and 2 with generalized epilepsy in patients of aged 4 to 80 years (median: 19 years; 25th–75th percentile: 14–32 years). Two-second nonoverlapping epochs with a 0.99 or higher IED probability were selected by a previously developed VGG-C convolutional neural network (CNN) as input for the second-level postprocessing CNN we developed. Our CNN was tested on the resulting 580 EEG epochs after 80/20 training/validation with 3,049 epochs.

Results

Model accuracy was 86% for the validation set and 60% for the test set. The first-level CNN selected 37% true IEDs, and with the addition of our second-level postprocessing CNN, this increased to 38%. Doubling input data of the second-level CNN, and making its architecture more complex, as well as less complex, did not improve performance.

Conclusion

We were unable to reproduce the previously reported performance of the first-level CNN, and adding the postprocessing CNN did not improve IED detection.

Keywords

deep learning - epilepsy - EEG - interictal epileptiform discharges - IED

Supplementary Material



Publication History

Article published online:
09 April 2025

© 2025. Indian Epilepsy Society. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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