A modular, closed-loop platform for intracranial stimulation in people with neurological disorders

Neuromodulation in Drug Resistant Epilepsy

Epilepsy affects approximately 70 million people worldwide, and it is a significant contributor to the global burden of neurological disorders. Despite the advent of new AEDs, drug resistant-epilepsy continues to affect 30-40% of PWE. Once identified as having drug-resistant epilepsy, these patients should be referred to a comprehensive epilepsy center for evaluation to establish if they are candidates for potential curative surgeries. Unfortunately, a large proportion of patients with drug-resistant epilepsy are poor surgical candidates due to a seizure focus located in eloquent cortex, multifocal epilepsy or inability to identify the zone of ictal onset. An alternative treatment modality for these patients is neuromodulation. Here we present the evidence, indications and safety considerations for the neuromodulation therapies in vagal nerve stimulation (VNS), responsive neurostimulation (RNS), or deep brain stimulation (DBS).

Deep learning for robust detection of interictal epileptiform discharges

OBJECTIVE

Automatic detection of interictal epileptiform discharges (IEDs, short as ``spikes'') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracortical EEG may facilitate online seizure monitoring and closed-loop neurostimulation.

APPROACH

We developed a new deep learning approach, which employs a long short-term memory (LSTM) network architecture (``IEDnet'') and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from intracranial electroencephalography (iEEG) recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances.

MAIN RESULTS

IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we also demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets.

SIGNIFICANCE

IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.