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Absence Seizure Control by a Brain Computer Interface.

Vladimir A Maksimenko¹, Sabrina van Heukelum², Vladimir V Makarov¹

¹REC 'Nonlinear Dynamics of Complex Systems', Yuri Gagarin State Technical University of Saratov, Polytechnicheskaya 77, Saratov, 410054, Russia.

Scientific Reports

|May 31, 2017

Summary

This summary is machine-generated.

Researchers developed a seizure prediction algorithm for absence epilepsy, successfully preventing 72% of seizures in rats. This breakthrough shows seizure prediction and closed-loop systems are feasible for controlling epilepsy.

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Area of Science:

Neuroscience
Epilepsy Research
Biomedical Engineering

Background:

Epileptology aims to completely abolish seizures, potentially through prediction and intervention systems.
Absence epilepsy seizures are challenging to predict due to their sudden onset.
Spike-wave discharges (SWDs) are characteristic of absence epilepsy.

Purpose of the Study:

To develop and evaluate a real-time absence seizure prediction algorithm.
To implement the algorithm in an online, closed-loop brain stimulation system.
To prevent SWDs in a genetic rat model of absence epilepsy.

Main Methods:

Developed a real-time seizure prediction algorithm for absence epilepsy.
Implemented the algorithm in a closed-loop brain stimulation system.
Tested the system on a genetic rat model, measuring SWD prediction and seizure activity reduction.

Main Results:

The algorithm correctly predicted 88% of SWDs, with others detected quickly.
False positive detections were reduced by adding criteria, though sensitivity decreased.
The closed-loop system reduced seizure activity by 72%.

Conclusions:

Absence epilepsy seizures (SWDs) are predictable, contrary to prior beliefs.
Closed-loop seizure prediction and prevention systems are feasible.
This approach offers a viable path towards seizure control and freedom for epilepsy patients.