Integrating brain implants with local and distributed computing devices: A next generation epilepsy management system

Vaclav Kremen, Benjamin H. Brinkmann, Inyong Kim, Hari Guragain, Mona Nasseri, Abigail L. Magee, Tal Pal Attia, Petr Nejedly, Vladimir Sladky, Nathanial Nelson, Su Youne Chang, Jeffrey A. Herron, Tom Adamski, Steven Baldassano, Jan Cimbalnik, Vince Vasoli, Elizabeth Fehrmann, Tom Chouinard, Edward E. Patterson, Brian LittMatt Stead, Jamie Van Gompel, Beverly K. Sturges, Hang Joon Jo, Chelsea M. Crowe, Timothy Denison, Gregory A. Worrell

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


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.

Original languageEnglish (US)
Article number8458201
JournalIEEE Journal of Translational Engineering in Health and Medicine
StatePublished - Sep 7 2018

Bibliographical note

Funding Information:
This work was supported in part by the Mayo Clinic Discovery Translation Grant, National Institutes of Health under Grant R01 NS09288203 and Grant UH2/UH3NS95495, in part by the Institutional Resources for Research by Czech Technical University in Prague, Czech Republic, in part by the Czech Science Foundation under Grant 1720480S, in part by the Temporal Context in Analysis of LongTerm NonStationary Multidimensional Signal, Czech Republic Grant Agency, under Grant P103/11/0933, in part by the European Regional Development Fund through the Project FNUSA-ICRC under Grant CZ.1.05/ 1.1.00/02.0123, and in part by the Mirowski Family Foundation.

Publisher Copyright:
© 2013 IEEE.


  • Epilepsy
  • deep brain stimulation
  • distributed computing
  • implantable devices
  • seizure detection
  • seizure prediction


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