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Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation
Abstract Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accura...
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| Published in: | Brain communications 2022, Vol.4 (3), p.fcac115-fcac115 |
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| creator | Sladky, Vladimir Nejedly, Petr Mivalt, Filip Brinkmann, Benjamin H Kim, Inyong St. Louis, Erik K Gregg, Nicholas M Lundstrom, Brian N Crowe, Chelsea M Attia, Tal Pal Crepeau, Daniel Balzekas, Irena Marks, Victoria S Wheeler, Lydia P Cimbalnik, Jan Cook, Mark Janca, Radek Sturges, Beverly K Leyde, Kent Miller, Kai J Van Gompel, Jamie J Denison, Timothy Worrell, Gregory A Kremen, Vaclav |
| description | Abstract
Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
Sladky et al. demonstrate accurate seizure diaries in dogs and humans receiving electrical stimulation for epilepsy while living in their home environments. Near real-time seizure diaries are created using an investigational device wirelessly streaming intracranial EEG to a handheld computer running a convolutional neural network with long- and short-term memory algorithm.
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| doi_str_mv | 10.1093/braincomms/fcac115 |
| format | article |
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Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
Sladky et al. demonstrate accurate seizure diaries in dogs and humans receiving electrical stimulation for epilepsy while living in their home environments. Near real-time seizure diaries are created using an investigational device wirelessly streaming intracranial EEG to a handheld computer running a convolutional neural network with long- and short-term memory algorithm.
Graphical Abstract
Graphical Abstract</description><identifier>ISSN: 2632-1297</identifier><identifier>EISSN: 2632-1297</identifier><identifier>DOI: 10.1093/braincomms/fcac115</identifier><identifier>PMID: 35755635</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Original</subject><ispartof>Brain communications, 2022, Vol.4 (3), p.fcac115-fcac115</ispartof><rights>Published by Oxford University Press on behalf of the Guarantors of Brain 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US. 2022</rights><rights>Published by Oxford University Press on behalf of the Guarantors of Brain 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-f061c5f1b8a601aaae2b466625abc9eb89881b4c0a6cc6c9716c824b06e2df4b3</citedby><cites>FETCH-LOGICAL-c440t-f061c5f1b8a601aaae2b466625abc9eb89881b4c0a6cc6c9716c824b06e2df4b3</cites><orcidid>0000-0002-2833-8826 ; 0000-0002-6151-043X ; 0000-0003-3456-9628 ; 0000-0002-2392-8608 ; 0000-0002-4712-7039 ; 0000-0003-4417-4240 ; 0000-0002-5310-5549 ; 0000-0001-8087-7870</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217965/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217965/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,315,733,786,790,891,1591,4043,27956,27957,27958,53827,53829</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35755635$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sladky, Vladimir</creatorcontrib><creatorcontrib>Nejedly, Petr</creatorcontrib><creatorcontrib>Mivalt, Filip</creatorcontrib><creatorcontrib>Brinkmann, Benjamin H</creatorcontrib><creatorcontrib>Kim, Inyong</creatorcontrib><creatorcontrib>St. Louis, Erik K</creatorcontrib><creatorcontrib>Gregg, Nicholas M</creatorcontrib><creatorcontrib>Lundstrom, Brian N</creatorcontrib><creatorcontrib>Crowe, Chelsea M</creatorcontrib><creatorcontrib>Attia, Tal Pal</creatorcontrib><creatorcontrib>Crepeau, Daniel</creatorcontrib><creatorcontrib>Balzekas, Irena</creatorcontrib><creatorcontrib>Marks, Victoria S</creatorcontrib><creatorcontrib>Wheeler, Lydia P</creatorcontrib><creatorcontrib>Cimbalnik, Jan</creatorcontrib><creatorcontrib>Cook, Mark</creatorcontrib><creatorcontrib>Janca, Radek</creatorcontrib><creatorcontrib>Sturges, Beverly K</creatorcontrib><creatorcontrib>Leyde, Kent</creatorcontrib><creatorcontrib>Miller, Kai J</creatorcontrib><creatorcontrib>Van Gompel, Jamie J</creatorcontrib><creatorcontrib>Denison, Timothy</creatorcontrib><creatorcontrib>Worrell, Gregory A</creatorcontrib><creatorcontrib>Kremen, Vaclav</creatorcontrib><title>Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation</title><title>Brain communications</title><addtitle>Brain Commun</addtitle><description>Abstract
Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
Sladky et al. demonstrate accurate seizure diaries in dogs and humans receiving electrical stimulation for epilepsy while living in their home environments. Near real-time seizure diaries are created using an investigational device wirelessly streaming intracranial EEG to a handheld computer running a convolutional neural network with long- and short-term memory algorithm.
Graphical Abstract
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Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
Sladky et al. demonstrate accurate seizure diaries in dogs and humans receiving electrical stimulation for epilepsy while living in their home environments. Near real-time seizure diaries are created using an investigational device wirelessly streaming intracranial EEG to a handheld computer running a convolutional neural network with long- and short-term memory algorithm.
Graphical Abstract
Graphical Abstract</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35755635</pmid><doi>10.1093/braincomms/fcac115</doi><orcidid>https://orcid.org/0000-0002-2833-8826</orcidid><orcidid>https://orcid.org/0000-0002-6151-043X</orcidid><orcidid>https://orcid.org/0000-0003-3456-9628</orcidid><orcidid>https://orcid.org/0000-0002-2392-8608</orcidid><orcidid>https://orcid.org/0000-0002-4712-7039</orcidid><orcidid>https://orcid.org/0000-0003-4417-4240</orcidid><orcidid>https://orcid.org/0000-0002-5310-5549</orcidid><orcidid>https://orcid.org/0000-0001-8087-7870</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Open Access: PubMed Central; AUTh Library subscriptions: Oxford University Press |
| subjects | Original |
| title | Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation |
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