Multi-branch convolutional neural network using intracranial EEG high frequency oscillation features for predicting post-surgical seizure outcomes

Pathological high-frequency oscillations (HFOs 80-600 Hz) in intracranial EEG distinguish epileptogenic cortex. However, it is uncertain whether utilizing HFO measures for surgical planning improve epilepsy surgery seizure outcomes and minimize morbidity. The clinical gold standard for planning an epilepsy surgery involves consensus between epileptologists, radiologists, and neurosurgeons based on multimodality findings, and particularly the location of the seizure onset zone. We asked whether seizure freedom following epilepsy surgery could be accurately predicted using machine learning that uses measures of HFO features relative to the boundaries of a surgical resection or laser ablation. We detected and quantified HFOs from depth EEG contacts during 30-200 minutes of non-rapid eye movement sleep from 78 pre-surgical patients from three institutions. We trained a three-branch convolutional neural network (CNN) using 3 neuroanatomic features and 37 HFO derived features. The first and second CNN branches computed within and between patient differences, respectively, and the third branch contains the resected contacts that also influenced branches 1 and 2. We found that this HFO CNN labeled the seizure free patients with 92% accuracy using 5-fold cross-validation. These results suggest that a resection planned with the clinical gold standard can be prospectively evaluated by a HFO CNN approach to test whether the resection boundaries will achieve a seizure free outcome. Future work will explore utilizing the HFO CNN approach for counterfactual virtual resections constrained by a utility function to minimize morbidity.