Graph convolutional network for generalized epileptiform abnormality detection on EEG

D. Nhu, M. Janmohamed, P. Perucca, A. Gilligan, P. Kwan, T. O'Brien, C. W. Tan, L. Kuhlmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Epilepsy diagnostic investigation involving manual visual analysis of electroencephalogram (EEG) is a time-consuming process. Deep neural networks, especially the convolutional network (CNN), have been applied to interictal epileptiform discharge (IED) detection and have achieved promising results. However, these networks do not incorporate clinical features of EEG montages. In recent years, graph convolution has succeeded in learning features from structural graph-like data. In this paper, we explore the novel application of different architectures of graph convolutions with Chebyshev polynomial filters which learn spatio-Temporal features from EEG montages. We conducted a number of experiments with transverse and longitudinal montages on a set of routine EEG recordings from patients with idiopathic generalized epilepsy. We split these EEG recordings into 2s windows with or without IED and evaluated different architectures in terms of how well they classified these windows. We achieved the best AUC of 0.92. Furthermore, we explored different thresholds of the output probability and observed that at 0.8, based on the selection of collected data, we achieved a mean false-positive rate per minute of 0.44 and still preserved a reasonable mean sensitivity of 0.64 across all EEG recordings. The results indicate that our approaches could produce clinically useful performance levels. Our work could be extended to improve the interpretability of the automated software in a clinical environment.

Original languageEnglish
Title of host publication2021 IEEE Signal Processing in Medicine and Biology Symposium, Proceedings
EditorsAlbert Kim
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665428972
Publication statusPublished - 2021
EventIEEE Signal Processing in Medicine and Biology Symposium 2021 - Philadelphia, United States of America
Duration: 4 Dec 20214 Dec 2021 (Proceedings)


ConferenceIEEE Signal Processing in Medicine and Biology Symposium 2021
Abbreviated titleSPMB 2021
Country/TerritoryUnited States of America
Internet address

Cite this