Hyperdimensional Computing with Local Binary Patterns: One-shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions using Short-time iEEG Recordings

Open access
Date
2020-02Type
- Journal Article
Citations
Cited 24 times in
Web of Science
Cited 28 times in
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ETH Bibliography
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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 to 100 electrodes. For the majority of the
patients (10 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% vs. 94.77%) and macroaveraging accuracy (95.42% vs. 94.96%), and 74x lower memory footprint, but slightly higher average latency in detection (15.9 s vs. 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; (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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000350002Publication status
publishedExternal links
Journal / series
IEEE Transactions on Biomedical EngineeringVolume
Pages / Article No.
Publisher
IEEESubject
Time series; Symbolic dynamics; Sizure detecOrganisational unit
03996 - Benini, Luca / Benini, Luca
Funding
608881 - ETH Zurich Postdoctoral Fellowship Program II (EC)
780215 - Computation-in-memory architecture based on resistive devices (EC)
Related publications and datasets
Is supplemented by: http://ieeg-swez.ethz.ch/
More
Show all metadata
Citations
Cited 24 times in
Web of Science
Cited 28 times in
Scopus
ETH Bibliography
yes
Altmetrics