A 1.5nJ/cls Unsupervised Online Learning Classifier for Seizure Detection

Abstract

This work presents a 1.5 nJ/classification (nJ/cls) seizure detection classifier which provides unsupervised online updates on an initial offline-trained regression model to achieve >97% average sensitivity and specificity on 27 patient datasets, including three that have >250 hours of continuous recording. The classifier was fabricated in 28nm CMOS and operates at 0.5V supply. Through hardware optimizations and low overall computational complexity and voltage scaling, the online learning classifier achieves 24× better energy per classification and occupies 10x lower area than state-of-the-art.