Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction

Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction

This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient’s quality of life as they can lead to paralyzation...

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Bibliographic Details
Main Authors: Vassilis Alimisis, Georgios Gennis, Konstantinos Touloupas, Christos Dimas, Nikolaos Uzunoglu, Paul P. Sotiriadis
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Bioengineering
Subjects:
analog VLSI implementation
analog wake-up
epileptic seizure
Gaussian mixture model
low-power design
seizure prediction
Online Access:https://www.mdpi.com/2306-5354/9/4/160

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https://www.mdpi.com/2306-5354/9/4/160

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