Not one size fits all: influence of EEG type when training a deep neural network for interictal epileptiform discharge detection

Objective: Deep learning methods have shown potential in automating interictal epileptiform discharge (IED) detection in electroencephalograms (EEGs). However, it is known that these algorithms are dependent on the type of data used for training and this is not currently explored in EEG analysis applications. We aim to explore the difference in performance of artificial neural networks on routine and ambulatory EEG data. Methods: We trained the same neural network on three datasets: 166 routine EEGs (VGGC-R), 75 ambulatory EEGs (VGGC-R) and a combination of the two data types (VGGC-C, 241 EEGs total). These networks were tested on 34 routine EEGs and 33 ambulatory recordings. Sensitivity, specificity and false positive rate (FPR) were calculated at a 0.99 probability threshold. Results: The VGGC-R led to 84% sensitivity at 99% specificity on the routine EEGs, but its sensitivity was only 53% on ambulatory EEGs, with FPR > 3 FP/min. The VGGC-C and VGGC-A yielded sensitivities of 79% and 60%, respectively, at 99% specificity on ambulatory data, but their sensitivity was under 60% for routine EEGs. Conclusion: We show that the VGGC-R should be used for routine recordings and the VGGC-C should be used for ambulatory recordings for IED detection. Significance: As different networks work better for different types of data, algorithms should be trained with the same type of EEG data they will be applied to, either routine or ambulatory. ### Competing Interest Statement M.J.A.M. van Putten is co-founder of Clinical Science Systems, a supplier of EEG systems for Medisch Spectrum Twente. Clinical Science Systems offered no funding and was not involved in the design, execution, analysis, interpretation or publication of the study. The remaining authors have no conflicts of interest.