One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
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Benini, LucaShow all
- Conference Paper
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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  for detecting 65 novel seizures with higher specificity and sensitivity, and lower memory footprint Show more
Book title2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Pages / Article No.
SubjectSeizure detection; Intracranial EEG; One-shot Learning; Implantable devices
Organisational unit03996 - Benini, Luca / Benini, Luca
608881 - ETH Zurich Postdoctoral Fellowship Program II (EC)
780215 - Computation-in-memory architecture based on resistive devices (EC)
NotesConference lecture on 19 October 2018.
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