An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection

We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into prototype vectors...

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Bibliographic Details
Main Authors: Benatti, S. (Author), Benini, L. (Author), Burrello, A. (Author), Rahimi, A. (Author), Schindler, K. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
accuracy
action potential
algorithm
Algorithms
anxiety
Article
artificial neural network
brain
Brain
classifier
diagnostic imaging
dynamics
electrode
Electrode selection
Electrodes
electroencephalography
Electroencephalography
electrostimulation
ensemble of classifiers
Ensemble of classifiers
epilepsy
epileptic patient
Epileptic seizures
human
Humans
hyperdimensional computing
ieeg
latent period
learning algorithm
line length
local binary patterns
Local binary patterns
Low computational complexity
low-latency seizure detection
machine learning
nerve cell network
Robustness to noise
seizure
Seizures
sensitivity and specificity
signal noise ratio
single photon emission computed tomography
Small memory footprint
speech intelligibility
State-of-the-art algorithms
support vector machine
symbolic dynamics
validation process
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