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 Litt, Matt Stead, Jamie Van Gompel, Beverly K Sturges, Hang Joon Jo, Chelsea M Crowe, Timothy Denison, Gregory A Worrell, 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 Litt, Matt Stead, Jamie Van Gompel, Beverly K Sturges, Hang Joon Jo, Chelsea M Crowe, Timothy Denison, Gregory A Worrell

Abstract

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.

Keywords: Epilepsy; deep brain stimulation; distributed computing; implantable devices; seizure detection; seizure prediction.

Figures

FIGURE 1.
FIGURE 1.
Schematic of next generation epilepsy management system using Medtronic investigational Summit RC+S. The implanted neural stimulator (INS) combines flexible stimulation paradigms, continuous telemetry of intracranial EEG, bi-directional communication between the INS and the Mayo Epilepsy Patient Assistant Device (EPAD) and provided by the Clinician Telemetry Module (CTM) and Summit RDK. The EPAD seamlessly interfaces with the INS and cloud to create a flexible platform with local and distributed computing, analytics, and data storage.
FIGURE 2.
FIGURE 2.
The Medtronic Investigational Summit RC+S system includes: implantable neural stimulator (INS), patient telemetry unit (PTM) and radio telemetry module (RTM) for wireless charging, clinician telemetry module (CTM) for wireless interface between INS and the epilepsy patient assistant device (EPAD) and research lab programmer (RLP). The RLP is used to program INS settings, stimulation and safety parameters. The CTM provides the interface with the EPAD and enables streaming of data from the INS to EPAD and to control the INS (closing the loop). The integrated system provides a flexible platform for device embedded, EPAD, cloud-based analytics and closed loop responsive stimulation.
FIGURE 3.
FIGURE 3.
A) Graphical representation of different sensing paradigms and battery depletion rates showing that smart sensing (red, yellow, and blue lines) can run approximately 2.5time longer than continuous recording (purple) while still capturing 100% of the seizures recorded with the embedded detector on. B) Battery level and battery consumption and charging trend shown in a Medtronic Digital Health Dashboard web application over approximately month of recording on one of the study subjects. It displays the approximately linear and regular discharging of the INS battery when maintaining the smart sensing paradigm.
FIGURE 4.
FIGURE 4.
Next Generation Epilepsy Management System: Canine with epilepsy undergoing continuous video-intracranial EEG monitoring and electrical brain stimulation. Top: Video of seizure onset captured on device. Middle: Automated seizure classification algorithm embedded on Summit RC+S captured seizure (green/red vertical line). Four channels of iEEG showing electrographic seizure onset. Bottom: Blow-up of channel showing seizure evolution.
FIGURE 5.
FIGURE 5.
A) Evoked related potential for subject 1. Top) Raw iEEG data captured on sensing electrode (1 kHz sampling rate) using stimulation parameters: frequency 2 Hz, pulse width , amplitude 3.0 mV (blue dots show automated detections of stimulation peaks). Bottom) Detected stimulation waveforms aligned by peak in stimulation artifact. Bold black line shows averaged waveform. B) Evoked related potentials during wake (Top) and deep sleep (middle) using continuous parahippocampus electrical stimulation and hippocampus sensing in canine #2. Bottom graph shows comparison of median curves calculated in awake versus in deep sleep. Note a difference in ERP delay in awake versus deep sleep.
FIGURE 6.
FIGURE 6.
A) Distribution of absolute power in six iEEG bands (2–4 Hz, 4–6 Hz, 8–12 Hz; 12 – 30 Hz, 30 – 55Hz, 65 – 115 Hz) for 12 30-second segments during stimulation for awake versus deep sleep (selected manually). Note several bands with no overlap in power. B) K-NN clustering to two clusters using six absolute power in band features (note only two features are displayed in 2-D graph). Clear separation between two classes (wake & deep sleep) was found using all six features. The class with higher delta power is assigned to deep sleep.

References

    1. Deeb al., “Proceedings of the fourth annual deep brain stimulation think tank: A review of emerging issues and technologies,” Front Integr. Neurosci., vol. 10, p. 38, Nov. 2016.
    1. Ezzyat al., “Direct brain stimulation modulates encoding states and memory performance in humans,” Current Biol., vol. 27, no. 9, pp. 1251–1258, 2017.
    1. Lozano A. al., “A phase II study of fornix deep brain stimulation in mild Alzheimer’s disease,” J. Alzheimer’s Disease, vol. 54, no. 2, pp. 777–787, 2016, doi: 10.3233/JAD-160017.
    1. Laxton A. al., “A phase I trial of deep brain stimulation of memory circuits in Alzheimer’s disease,” Ann. Neurol., vol. 68, no. 4, pp. 521–534, 2010.
    1. Mayberg H. al., “Deep brain stimulation for treatment-resistant depression,” Neuron, vol. 45, no. 5, pp. 651–660, 2005.
    1. Raymaekers S., Luyten L., Bervoets C., Gabriëls L., and Nuttin B., “Deep brain stimulation for treatment-resistant major depressive disorder: A comparison of two targets and long-term follow-up,” Transl. Psychiatry, vol. 7, no. 10, p. e1251, 2017.
    1. Langevin al., “Deep brain stimulation of the basolateral amygdala for treatment-refractory posttraumatic stress disorder,” Biol. Psychiatry, vol. 79, no. 10, pp. e82–e84, 2016.
    1. Butson C. R., Tamm G., Jain S., Fogal T., and Krüger J., “Evaluation of interactive visualization on mobile computing platforms for selection of deep brain stimulation parameters,” IEEE Trans. Vis. Comput. Graphics vol. 19, no. 1, pp. 108–117, Jan. 2013.
    1. Boyden E., Allen B., and Fritz D., “Brain coprocessors: The need for operating systems to help brains and machines work together,” Intell. Mach., Technol. Rev., Sep. 2010. [Online]. Available:
    1. Téllez-Zenteno J. F., Dhar R., and Wiebe S., “Long-term seizure outcomes following epilepsy surgery: A systematic review and meta-analysis,” Brain, vol. 128, pp. 1188–1198, May 2005.
    1. Jehi L., Wyllie E., and Devinsky O., “Epileptic encephalopathies: Optimizing seizure control and developmental outcome,” Epilepsia, vol. 56, no. 10, pp. 1486–1489, Oct. 2015, doi: 10.1111/epi.13107.
    1. Fisher R. S. and Velasco A. L., “Electrical brain stimulation for epilepsy,” Nature Rev. Neurol., vol. 10, pp. 261–270, 2014.
    1. Bourget al., “An implantable, rechargeable neuromodulation research tool using a distributed interface and algorithm architecture,” in Proc. 7th Int. IEEE/EMBS Conf. Neural Eng. (NER), Montpellier, France, Apr. 2015, pp. 61–65. [Online]. Available: , doi: 10.1109/NER.2015.7146560.
    1. Fisher al., “Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy,” Epilepsia, vol. 51, no. 5, pp. 899–908, 2015.
    1. Salanova al., “Long-term efficacy and safety of thalamic stimulation for drug-resistant partial epilepsy,” Neurology, vol. 84, no. 10, pp. 1017–1025, 2015.
    1. Morrell M. J., “Responsive cortical stimulation for the treatment of medically intractable partial epilepsy,” Neurology, vol. 77, no. 13, pp. 1295–1304, 2011.
    1. Heck C. al., “Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: Final results of the RNS system pivotal trial,” Epilepsia, vol. 55, no. 3, pp. 432–441, 2014.
    1. Bergey G. al., “Long-term treatment with responsive brain stimulation in adults with refractory partial seizures,” Neurology, vol. 84, no. 8, pp. 810–817, 2015.
    1. Hoppe C., Poepel A., and Elger C. E., “Epilepsy: Accuracy of patient seizure counts,” Arch. Neurol., vol. 64, no. 11, pp. 1595–1599, 2007.
    1. Cook M. al., “Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: A first-in-man study,” Lancet Neurol., vol. 12, no. 6, pp. 563–571, 2013, doi: 10.1016/S14744422(13)700759.
    1. Elger C. E. and Mormann F., “Seizure prediction and documentation—Two important problems,” Lancet Neurol., vol. 12, no. 6, pp. 531–532, 2013.
    1. Osorio I., “The NeuroPace trial: Missing knowledge and insights,” Epilepsia, vol. 55, no. 9, pp. 1469–1470, 2014.
    1. Morrell M. J., “In response: The RNS System multicenter randomized double-blinded controlled trial of responsive cortical stimulation for adjunctive treatment of intractable partial epilepsy: Knowledge and insights gained,” Epilepsia, vol. 55, no. 9, pp. 1470–1471, 2014.
    1. Davis K. al., “A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG,” Epilepsy Res., vol. 96, nos. 1–2, pp. 116–122, 2011.
    1. Howbert J. al., “Forecasting seizures in dogs with naturally occurring epilepsy,” PLoS ONE, vol. 9, no. 1, p. e81920, 2014.
    1. Brinkmann B. al., “Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy,” PLoS ONE, vol. 10, no. 8, p. e0133900, 2015.
    1. Brinkmann B. al., “Crowdsourcing reproducible seizure forecasting in human and canine epilepsy,” Brain, vol. 139, no. 6, pp. 1713–1722, 2016.
    1. Kremen al., “Behavioral state classification in epileptic brain using intracranial electrophysiology,” J. Neural Eng., vol. 14, no. 2, p. 026001, 2017, doi: 10.1088/17412552/aa5688.
    1. Lundstrom B. N., Worrell G. A., Stead M., and Van Gompel J. J., “Chronic subthreshold cortical stimulation: A therapeutic and potentially restorative therapy for focal epilepsy,” Expert Rev. Neurotherapeutics, vol. 17, no. 7, pp. 661–666, 2017, doi: 10.1080/14737175.2017.1331129.
    1. Karoly P. J., Nurse E. S., Freestone D. R., Ung H., Cook M. J., and Boston R., “Bursts of seizures in long-term recordings of human focal epilepsy,” Epilepsia, vol. 58, no. 3, pp. 363–372, 2017.
    1. Worrell G. A., Jerbi K., Kobayashi K., Lina J. M., Zelmann R., and Le Van Quyen M., “Recording and analysis techniques for high-frequency oscillations,” Prog. Neurobiol., vol. 98, no. 3, pp. 265–278, 2012.
    1. Valentín al., “Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo,” Brain, vol. 125, no. 8, pp. 1709–1718, 2002.
    1. Valentín al., “Single pulse electrical stimulation for identification of structural abnormalities and prediction of seizure outcome after epilepsy surgery: A prospective study,” Lancet Neurol., vol. 4, no. 11, pp. 718–726, 2005.
    1. Kalitzin S., Velis D., Suffczynski P., Parra J., and da Silva F. L., “Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy,” Clin. Neurophysiol., vol. 116, no. 3, pp. 718–728, 2005.
    1. Baldassano S. al., “Crowdsourcing seizure detection: Algorithm development and validation on human implanted device recordings,” Brain, vol. 140, no. 6, pp. 1680–1691, 2017.
    1. Bower M. al., “Evidence for consolidation of neuronal assemblies after seizures in humans,” J. Neurosci., vol. 35, no. 3, pp. 999–1010, 2015, doi: 10.1523/JNEUROSCI.301914.
    1. Bower M. al., “Reactivation of seizure-related changes to interictal spike shape and synchrony during postseizure sleep in patients,” Epilepsia, vol. 58, no. 1, pp. 94–104, 2017, doi: 10.1111/epi.13614.
    1. Sarkisian M. R., “Overview of the current animal models for human seizure and epileptic disorders,” Epilepsy Behav., vol. 2, no. 3, pp. 201–216, 2001.
    1. Berendt M. and Gram L., “Epilepsy and seizure classification in 63 dogs: A reappraisal of veterinary epilepsy terminology,” J. Vet. Internal Med., vol. 13, no. 1, pp. 14–20, 1999.
    1. Chandler K., “Canine epilepsy: What can we learn from human seizure disorders?” Vet. J., vol. 172, no. 2, pp. 207–217, 2006.
    1. Dewey C. al., “Zonisamide therapy for refractory idiopathic epilepsy in dogs,” J. Amer. Animal Hospital Assoc., vol. 40, no. 4, pp. 285–291, 2004.
    1. Leppik I. E., Patterson E., Hardy B., and Cloyd J. C., “Canine status epilepticus: Proof of principle studies,” Epilepsia, vol. 50, pp. 14–15, Dec. 2009.
    1. Lundstrom B. al., “Chronic subthreshold cortical stimulation to treat focal epilepsy,” JAMA Neurol., vol. 73, no. 11, pp. 1370–1372, Nov. 2016.
    1. Volk H. A., Matiasek L. A., Feliu-Pascual A. L., Platt S. R., and Chandler K. E., “The efficacy and tolerability of levetiracetam in pharmacoresistant epileptic dogs,” Vet J., vol. 176, no. 3, pp. 310–319, 2008.
    1. Jeserevics al., “Electroencephalography findings in healthy and Finnish Spitz dogs with epilepsy: Visual and background quantitative analysis,” J. Vet. Internal Med., vol. 21, no. 6, pp. 1299–1306, 2007.
    1. Berendt M., Høgenhaven H., Flagstad A., and Dam M., “Electroencephalography in dogs with epilepsy: Similarities between human and canine findings,” Acta Neurol. Scandinavica, vol. 99, no. 5, pp. 276–283, 1999.
    1. Pellegrino F. C. and Sica R. E. P., “Canine electroencephalographic recording technique: Findings in normal and epileptic dogs,” Clin. Neurophysiol., vol. 115, no. 2, pp. 477–487, 2004.
    1. Snyder D. E., Echauz J., Grimes D. B., and Litt B., “The statistics of a practical seizure warning system,” J. Neural Eng., vol. 5, no. 4, pp. 392–401, 2008, doi: 10.1088/17412560/5/4/004.
    1. Sun F. T. and Morrell M. J., “The RNS system: Responsive cortical stimulation for the treatment of refractory partial epilepsy,” Expert Rev. Med. Devices, vol. 11, no. 6, pp. 563–572, 2014, doi: 10.1586/17434440.2014.947274.
    1. Coles L. al., “Feasibility study of a caregiver seizure alert system in canine epilepsy,” Epilepsy Res., vol. 106, no. 3, pp. 456–460, 2013.

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