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

Burrello, Alessio; Schindler, Kaspar Anton; Benini, Luca; Rahimi, Abbas (2020). Hyperdimensional Computing with Local Binary Patterns: One-shot Learning for Seizure Onset Detection and Identification of Ictogenic Brain Regions from Short-time iEEG Recordings. IEEE transactions on bio-medical engineering, 67(2), pp. 601-613. Institute of Electrical and Electronics Engineers 10.1109/TBME.2019.2919137

Text Burello, 2019, Hyperdimensional Computing with Local binary Patterns.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (2MB)

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 supports for clinical decision making.

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Citations

5 Citations in Web of Science ®
6 Citations in Scopus

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