Many technologies have been proposed for treating epileptic seizures, with the ultimate goal being implantation of stimulators or drug infusion devices in the brain to abort seizures before clinical onset. Device designs range from “blind” stimulators to “intelligent” devices, which are triggered by detecting or predicting seizure onset. Intelligent implantable epilepsy devices will likely process multiple channels of data, be tuned to individual patients, and may need to predict events rather than merely detect them.

This schematic of an Intelligent Epilepsy Device includes data acquisition, preprocessing,feature extraction, and classification stages.
It has been suggested that a central functional structure deep in the brain is responsible for propagating epileptic seizures from discrete, electrically unstable areas, or foci, to broad regions of the cerebral cortex. Research has reinforced the idea that seizures likely spread through discrete, functional neuronal networks. Sorting out how and where to intervene with electrical stimulation or drug infusion to treat which type(s) of human epilepsy is one of the major challenges in developing implantable antiepileptic devices.

The vagal nerve stimulator (VNS) is the first implantable medical device approved by the FDA for the treatment of epilepsy. This device consists of an implantable, pacemaker-like stimulation unit implanted under the clavicle, connected to an electrode wrapped around the vagus nerve on the left side of the neck. The device reduces seizure frequency by an average of 20-30% in most individuals, with an approximately 10% chance of being seizure-free.

Recent research suggests that epileptic seizures, particularly in the temporal lobe, may begin up to hours prior to their electrical onset. A variety of computational methods have been proposed for measuring these changes, ranging from non-linear dynamics, to linear measures extracted from the EEG, to combinations of multiple parameters. These algorithms complement other signal processing methods to rapidly detect seizure onset on EEG, which can be used to trigger therapeutic intervention.

Epilepsy devices are both more complex and subject to lower tolerance for side effects than their cardiologic analogs. Rather than prevent death, these devices are intended to restore normal life and behavior. Error tolerance in event detection will be similarly low. Seizure detection and prediction algorithms are likely to be “tuned” to the individual patients for maximal performance.

Development of better algorithms to detect and predict seizures is proceeding in parallel with new technologies to arrest seizures. Early positive results of animal experiments are forming the foundation upon which pilot human trials are based. Accepted and FDA-approved cardiologic de vices are providing models for the development of neurological implants, and experience with similar devices for Parkinson’s disease are accelerating development. New techniques for seizure detection and prediction will likely en able individually trained, customized, intelligent de vices. These devices, though more complex than “blind” stimulating devices, may have the potential to demonstrate greater efficacy in the long term, provided that the side effects of brain stimulation in the region of the epileptic focus are acceptable.

This work was done by Dr. Brian Litt of the University of Pennsylvania for the Army Research Laboratory. For more information, download the Technical Support Package (free white paper) at  under the Bio-Medical category. ARL-0069

This Brief includes a Technical Support Package (TSP).
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Engineering Devices To Treat Epilepsy

(reference ARL-0069) is currently available for download from the TSP library.

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