Predicting seizures from local field potentials recorded via intracortical microelectrode arrays

The need for new therapeutic interventions to treat pharmacologically resistant focal epileptic seizures has led recently to the development of closed-loop systems for seizure control. Once a seizure is predicted/detected by the system, electrical stimulation is delivered to prevent seizure initiation or spread. So far, seizure prediction/detection has been limited to tracking non-invasive electroencephalogram (EEG) or intracranial EEG (iEEG) signals. Here, we examine seizure prediction based on local field potentials (LFPs) from a small neocortical patch recorded via a 10×10 microelectrode array implanted in a patient with focal seizures. We formulate the seizure (ictal) prediction problem in terms of discriminating between interictal and preictal neural activity. Using deep Convolutional Neural Networks (CNNs), we show that periods of preictal activity can be successfully discriminated (80% detection; no false positives) from periods of interictal activity several (2-18) minutes prior to seizure onset. CNN input features consisted of the spectral power of LFP channels (1-second time windows) computed in 50 frequency bands (0-100 Hz; 2 Hz steps). Our preliminary results show that intracortical LFPs may be a promising neural signal for seizure prediction in focal epilepsy.