Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings.
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| Title: | Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. |
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| Authors: | Burrello, Alessio1 ieeg@iis.ee.ethz.ch, Schindler, Kaspar2, Benini, Luca3 lbenini@iis.ee.ethz.ch, Rahimi, Abbas3 abbas@ee.ethz.ch |
| Source: | IEEE Transactions on Biomedical Engineering. Feb2020, Vol. 67 Issue 2, p601-613. 13p. |
| Subjects: | Seizures (Medicine), Symbolic dynamics, Cerebral hemispheres, Binary operations, Binary codes, Distributed algorithms, End-to-end delay |
| Abstract: | Objective: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Methods: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. Results: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36–100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using $k$ -fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a $p$ -value $< 0.01$) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Conclusion and significance: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 141418548 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burrello%2C+Alessio%22">Burrello, Alessio</searchLink><relatesTo>1</relatesTo><i> ieeg@iis.ee.ethz.ch</i><br /><searchLink fieldCode="AR" term="%22Schindler%2C+Kaspar%22">Schindler, Kaspar</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Benini%2C+Luca%22">Benini, Luca</searchLink><relatesTo>3</relatesTo><i> lbenini@iis.ee.ethz.ch</i><br /><searchLink fieldCode="AR" term="%22Rahimi%2C+Abbas%22">Rahimi, Abbas</searchLink><relatesTo>3</relatesTo><i> abbas@ee.ethz.ch</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Biomedical+Engineering%22">IEEE Transactions on Biomedical Engineering</searchLink>. Feb2020, Vol. 67 Issue 2, p601-613. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Seizures+%28Medicine%29%22">Seizures (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Symbolic+dynamics%22">Symbolic dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Cerebral+hemispheres%22">Cerebral hemispheres</searchLink><br /><searchLink fieldCode="DE" term="%22Binary+operations%22">Binary operations</searchLink><br /><searchLink fieldCode="DE" term="%22Binary+codes%22">Binary codes</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+algorithms%22">Distributed algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22End-to-end+delay%22">End-to-end delay</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Methods: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. Results: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36–100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using $k$ -fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a $p$ -value $< 0.01$) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Conclusion and significance: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TBME.2019.2919137 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 601 Subjects: – SubjectFull: Seizures (Medicine) Type: general – SubjectFull: Symbolic dynamics Type: general – SubjectFull: Cerebral hemispheres Type: general – SubjectFull: Binary operations Type: general – SubjectFull: Binary codes Type: general – SubjectFull: Distributed algorithms Type: general – SubjectFull: End-to-end delay Type: general Titles: – TitleFull: Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burrello, Alessio – PersonEntity: Name: NameFull: Schindler, Kaspar – PersonEntity: Name: NameFull: Benini, Luca – PersonEntity: Name: NameFull: Rahimi, Abbas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 00189294 Numbering: – Type: volume Value: 67 – Type: issue Value: 2 Titles: – TitleFull: IEEE Transactions on Biomedical Engineering Type: main |
| ResultId | 1 |
