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Research Area: Machine Learning
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.
Keywords:
One-Shot Learning
Ieeg Seizure Detection
End-To-End Binary Operation
Hyperdimensional Computing
Machine Learning
Deep Learning
Author(s) Name: Alessio Burrello; Kaspar Schindler; Luca Benini; Abbas Rahimi
Journal name:
Conferrence name: IEEE Biomedical Circuits and Systems Conference (BioCAS)
Publisher name: IEEE
DOI: 10.1109/BIOCAS.2018.8584751
Volume Information:
