SPIRIT: A Seizure Prediction SoC with a 17.2nJ/cls Unsupervised Online-Learning Classifier and Zoom Analog Frontends

This work presents SPIRIT, an SoC integrating an unsupervised online-learning seizure prediction classifier with eight
$14.4\mu \mathrm{W},\ 0.057\text{mm}^{2},\ 90.5\text{dB}$
dynamic range, Zoom Analog Frontends. SPIRIT achieves, on average, 97.5%/96.2% sensitivity/specificity, predicting seizures an average of 8.4 minutes before they occur. Its classifier consumes
$17.2\mu \mathrm{W}$
and occupies 0.14mm2, the lowest reported for a prediction classifier by
$> 134\mathrm{x}$
in power and
$> 5\mathrm{x}$
in area.