Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Epilepsy Research

Background:

• Early epilepsy therapy devices lacked sensing and patient input.
• Current devices sense brain activity but lack seizure quantification analytics.
• Accurate seizure tracking is crucial for epilepsy therapy and optimizing brain stimulation.

Purpose of the Study:

• To describe a novel distributed brain co-processor system.
• To enable intuitive bi-directional interface for epilepsy management.
• To automate quantification of seizures and interictal epileptiform spikes.

Main Methods:

• Developed a co-processor with local and cloud computing resources.
• Integrated automated electrophysiology analysis with patient handheld device reports.
• Used long-term ambulatory data from humans and canines for algorithm development.
• Prospectively tested algorithms in humans and canines with drug-resistant epilepsy.

Main Results:

• The system enables automated analysis of electrophysiology synchronized with patient reports.
• Algorithms accurately classify interictal epileptiform spikes and seizures.
• Prospective testing demonstrated system utility in natural environments.

Conclusions:

• The brain co-processor system facilitates accurate tracking of epilepsy seizures and spikes.
• It enables correlation of neurological events with patient-reported symptoms and behavior.
• This technology can advance epilepsy therapy and personalized brain stimulation.