Application of an EEG-based deep learning model to discriminate children with epileptic spasms from normal controls

ObjectiveGiven that epileptic spasms are often subtle, and that identification of hypsarrhythmia is limited by inadequate inter-rater reliability, there is a significant need for novel tools to aid the clinical identification of Infantile Epileptic Spasms Syndrome (IESS). Deep learning is an emerging technology which may enable efficient classification of disease states and may facilitate discovery of novel biomarkers. In this study, we set out to evaluate whether children with epileptic spasms can be distinguished from normal controls with use of an EEG-based deep learning model. MethodsA deep learning model was trained and validated (5-fold cross validation) using 400 EEG samples (2 awake and 2 sleep samples from 50 children with epileptic spasms and 50 normal controls). Salient frequency bands and specific morphologic EEG features were identified with occlusion sensitivity analysis and targeted input perturbation, respectively. ResultsThe model accurately distinguishes children with epileptic spasms from normal controls, solely on the basis of relatively short EEG samples. Using sleep data, accuracy = 0.95, recall = 0.96, precision (sensitivity) = 0.94, specificity = 0.94, and F1 score = 0.95. With awake data, accuracy = 0.91, recall = 0.84, precision = 0.98, specificity = 0.98, and F1 score = 0.90. The salient frequency bands for classification are 9.7 - 22.0 Hz and 1.0 - 6.8 Hz in sleep and awake EEG, respectively. With visual analysis of extracted salient features, we suspect that the model is identifying cases on the basis of paroxysmal fast activity in sleep and spike-wave activity in wakefulness. ConclusionThis deep learning model represents a first step in the development of efficient algorithms that may aid in identification of epileptic spasms and IESS. More importantly, this approach may facilitate novel EEG-based biomarkers of epileptic spasms.