Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F18%3A00069333" target="_blank" >RIV/00159816:_____/18:00069333 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/18:00324742
Result on the web
<a href="http://dx.doi.org/10.1109/JTEHM.2018.2869398" target="_blank" >http://dx.doi.org/10.1109/JTEHM.2018.2869398</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JTEHM.2018.2869398" target="_blank" >10.1109/JTEHM.2018.2869398</a>
Alternative languages
Result language
angličtina
Original language name
Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System
Original language description
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 off the body 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM
ISSN
2168-2372
e-ISSN
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Volume of the periodical
6
Issue of the periodical within the volume
2018
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
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UT code for WoS article
000446248500001
EID of the result in the Scopus database
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