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dc.contributor.author
Burrello, Alessio
dc.contributor.author
Schindler, Kaspar
dc.contributor.author
Benini, Luca
dc.contributor.author
Rahimi, Abbas
dc.date.accessioned
2020-10-05T09:13:27Z
dc.date.available
2019-05-14T11:01:53Z
dc.date.available
2020-10-05T09:13:27Z
dc.date.issued
2018
dc.identifier.isbn
978-1-5386-3603-9
en_US
dc.identifier.isbn
978-1-5386-3604-6
en_US
dc.identifier.other
10.1109/BIOCAS.2018.8584751
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/342065
dc.identifier.doi
10.3929/ethz-b-000305686
dc.description.abstract
This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG). The algorithm combines local binary patterns with brain-inspired hyperdimensional computing to enable end-to-end learning and inference with binary operations. The algorithm first transforms iEEG time series from each electrode into local binary pattern codes. Then atomic high-dimensional binary vectors are used to construct composite representations of seizures across all electrodes. For the majority of our patients (10 out of 16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot learning) and perfectly generalizes on 27 further seizures. For other patients, the algorithm requires three to six seizures for learning. Overall, our algorithm surpasses the state-of-the-art methods [1] for detecting 65 novel seizures with higher specificity and sensitivity, and lower memory footprint.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Seizure detection
en_US
dc.subject
Intracranial EEG
en_US
dc.subject
One-shot Learning
en_US
dc.subject
Implantable devices
en_US
dc.title
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2018-12-24
ethz.book.title
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
en_US
ethz.pages.start
475
en_US
ethz.pages.end
478
en_US
ethz.size
4 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
en_US
ethz.event.location
Cleveland, OH, USA
en_US
ethz.event.date
October 17-19, 2018
en_US
ethz.notes
Conference lecture on 19 October 2018.
en_US
ethz.grant
ETH Zurich Postdoctoral Fellowship Program II
en_US
ethz.grant
Computation-in-memory architecture based on resistive devices
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.tag
Local binary patterns
en_US
ethz.tag
Hyperdimensional computing
en_US
ethz.tag
Symbolization
en_US
ethz.grant.agreementno
608881
ethz.grant.agreementno
780215
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
FP7
ethz.grant.program
H2020
ethz.date.deposited
2018-11-22T14:58:27Z
ethz.source
FORM
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-05-14T11:02:02Z
ethz.rosetta.lastUpdated
2022-03-29T03:16:57Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/305686
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/323978
ethz.COinS
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