Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články
Grantová podpora
R01 NS092882
NINDS NIH HHS - United States
UH2 NS095495
NINDS NIH HHS - United States
PubMed
30310759
PubMed Central
PMC6170139
DOI
10.1109/jtehm.2018.2869398
PII: 2500112
Knihovny.cz E-zdroje
- Klíčová slova
- Epilepsy, deep brain stimulation, distributed computing, implantable devices, seizure detection, seizure prediction,
- Publikační typ
- časopisecké články MeSH
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
Department of NeurologyMayo ClinicRochesterMN55905USA
Department of NeurosurgeryMayo ClinicRochesterMN55905USA
Department of Physiology and Biomedical EngineeringMayo ClinicRochesterMN55905USA
Department of Surgical and Radiological SciencesUniversity of California at DavisDavisCA95616USA
International Clinical Research CenterSt Anne's University Hospital656 91BrnoCzech Republic
Mayo Systems Electrophysiology LaboratoryDepartment of NeurologyMayo ClinicRochesterMN55905USA
Research and Core TechnologyRestorative Therapy Group MedtronicMinneapolisMN55432 3568USA
Veterinary Medical Teaching HospitalUniversity of California at DavisDavisCA95616USA
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