Winter Meeting Dutch Society of Clinical Neurophysiology Deep Learning for Detection of Epileptiform Discharges in Routine EEG

Vesna Miljanovic 1* , Michel J.A.M. van Putten 1,2 1 Clinical Neurophysiology, MIRA – Institute for Biomedical Engineering and Technical Medicine, University of Twente, The Netherlands 2 Department of Clinical Neurophysiology, Medish Spectrum Twente, Enschede, The Netherlands * [email protected]

Abstract

Introduction:

Since the beginning of the 20th century, electroencephalography has been standard clinical procedure for diagnostics in epilepsy, and visual analysis for the detection of epileptiform discharges is gold standard. While several methods for automatic EEG analysis have been proposed actual clinical implementation of these tools is limited mainly due to moderate specificity. We propose a novel approach for the detection of interictal discharges using deep learning with convolutional neural networks.

Methods:

Interictal discharges of eight 19-channel routine EEG recordings from different patients diagnosed with focal epilepsy were annotated by experts. Datasets for training, validation and testing consisted of 2-second long raw EEG signals. Two convolutional layers followed by maxpooling layers were built in a classifier. A dropout layer was included to prevent overtraining. Output of the network was a binary label, detecting presence or absence of epileptiform discharges. Performance was evaluated using sensitivity and specificity by leave-one-out method.

Results:

The total number of annotations was 450 of which 283 contained discharges. Training of the network took 2 min, whereas testing only 1.2 s. A signal with the best performance reached sensitivity 81.00% and specificity 70.00%, while poorest performance was 46.67% sensitivity and 47.67% specificity.

Conclusion:

network.

In this pilot study, CNNs showed promising results in the detection of interictal discharges on limited data. We foresee enlarging the dataset will significantly improve performance of the