Efficient use of clinical EEG data for deep learning in epilepsy

Catarina Da Silva Lourenço,Marleen C Tjepkema-Cloostermans,Michel J.A.M Van Putten

doi:10.1016/j.clinph.2021.01.035

Abstract

ObjectiveAutomating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but the scarceness of expert annotated data creates a bottleneck in the process. MethodsWe used EEGs from 50 patients with focal epilepsy, 49 patients with generalized epilepsy (IEDs were visually labeled by experts) and 67 controls. The data was filtered, downsampled and cut into two second epochs. We increased the number of input samples containing IEDs through temporal shifting and using different montages. A VGG C convolutional neural network was trained to detect IEDs. ResultsUsing the dataset with more samples, we reduced the false positive rate from 2.11 to 0.73 detections per minute at the intersection of sensitivity and specificity. Sensitivity increased from 63% to 96% at 99% specificity. The model became less sensitive to the position of the IED in the epoch and montage. ConclusionsTemporal shifting and use of different EEG montages improves performance of deep neural networks in IED detection. SignificanceDataset augmentation can reduce the need for expert annotation, facilitating the training of neural networks, potentially leading to a fundamental shift in EEG analysis.

Highlights

Interictal epileptiform discharges (IEDs) are transient patterns that reflect an increased likelihood of epileptic seizures (Pillai and Sperling, 2006; Smith, 2005)
With the original dataset (Set A), the intersection between sensitivity and specificity occurred at 93% with a false positive rate of 2.11 (0.85–3.38) false detections per minute
We studied the effect of temporal shifting and the use of different montages to increase the number of samples used to train a VGG C network for IED detection

Summary

Introduction

Interictal epileptiform discharges (IEDs) are transient patterns that reflect an increased likelihood of epileptic seizures (Pillai and Sperling, 2006; Smith, 2005). Visual analysis of EEG signals by experts is currently the gold standard in IED detection (Lodder et al, 2014), but this approach entails several disadvantages. The learning curve is long, review times are significant and. Automating IED detection can reduce the resources spent on visual analysis, time to diagnosis and the misdiagnosis rate. Most are based on ’pre-chosen’ features, which might limit the algorithm’s ability to learn how to detect these transients and seem to justify the moderate performance of these algorithms. Endto-end deep learning approaches have been used (TjepkemaCloostermans et al, 2018; Lourenço et al, 2020; Johansen et al, 2016; Jing et al, 2019), which can learn their own representation of the feature space from raw data (LeCun et al, 2015)

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