Towards real-time in-implant epileptic seizure prediction

Online Seizure Prediction System: A Novel Probabilistic Approach for Efficient Prediction of Epileptic Seizure with iEEG Signal

Background:

1% of people around the world are suffering from epilepsy. It is, therefore crucial to propose an efficient automated seizure prediction tool implemented in a portable device that uses the electroencephalogram (EEG) signal to enhance epileptic patients’ life quality.

Methods:

In this study, we focused on time-domain features to achieve discriminative information at a low CPU cost extracted from the intracranial electroencephalogram (iEEG) signals of six patients. The probabilistic framework based on XGBoost classifier requires the mean and maximum probability of the non-seizure and the seizure occurrence period segments. Once all these parameters are set for each patient, the medical decision maker can send alarm based on well-defined thresholds.

Results:

While finding a unique model for all patients is really challenging, and our modelling results demonstrated that the proposed algorithm can be an efficient tool for reliable and clinically relevant seizure forecasting. Using iEEG signals, the proposed algorithm can forecast seizures, informing a patient about 75 minutes before a seizure would occur, a period large enough for patients to take practical actions to minimize the potential impacts of the seizure.

Conclusion:

We posit that the ability to distinguish interictal intracranial EEG from pre-ictal signals at some low computational cost may be the first step towards an implanted portable semi-automatic seizure suppression system in the near future. It is believed that our seizure prediction technique can conceivably be coupled with treatment techniques aimed at interrupting the process even prior to a seizure initiates to develop.

Optogenetic control of epileptiform activity

The optogenetic approach to gain control over neuronal excitability both in vitro and in vivo has emerged as a fascinating scientific tool to explore neuronal networks, but it also opens possibilities for developing novel treatment strategies for neurologic conditions. We have explored whether such an optogenetic approach using the light-driven halorhodopsin chloride pump from Natronomonas pharaonis (NpHR), modified for mammalian CNS expression to hyperpolarize central neurons, may inhibit excessive hyperexcitability and epileptiform activity. We show that a lentiviral vector containing the NpHR gene under the calcium/calmodulin-dependent protein kinase IIalpha promoter transduces principal cells of the hippocampus and cortex and hyperpolarizes these cells, preventing generation of action potentials and epileptiform activity during optical stimulation. This study proves a principle, that selective hyperpolarization of principal cortical neurons by NpHR is sufficient to curtail paroxysmal activity in transduced neurons and can inhibit stimulation train-induced bursting in hippocampal organotypic slice cultures, which represents a model tissue of pharmacoresistant epilepsy. This study demonstrates that the optogenetic approach may prove useful for controlling epileptiform activity and opens a future perspective to develop it into a strategy to treat epilepsy.