Deep learning for detection of focal epileptiform discharges from scalp EEG recordings

Objective: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promising novel approach, being able to learn from large datasets. Here, we show pilot results of detecting epileptiform discharges using deep neural networks.

Methods: We selected 50 EEGs from focal epilepsy patients. All epileptiform discharges (n = 1815) were annotated by an experienced neurophysiologist and extracted as 2 s epochs. In addition, 50 normal EEGs were divided into 2 s epochs. All epochs were divided into a training (n = 41,381) and test (n = 8775) set. We implemented several combinations of convolutional and recurrent neural networks, providing the probability for the presence of epileptiform discharges. The network with the largest area under the ROC curve (AUC) in the test set was validated on seven independent EEGs with focal epileptiform discharges and twelve normal EEGs.

Results: The final network had an AUC of 0.94 for the test set. Validation allowed detection of epileptiform discharges with 47.4% sensitivity and 98.0% specificity (FPR: 0.6/min). For the normal EEGs in the validation set, the specificity was 99.9% (FPR: 0.03/min).

Conclusions: Deep neural networks can accurately detect epileptiform discharges from scalp EEG recordings.

Significance: Deep learning may result in a fundamental shift in clinical EEG analysis.