  This work explores the potential utility of neural network classifiers for
real-time classification of field-potential based biomarkers in next-generation
responsive neuromodulation systems. Compared to classical filter-based
classifiers, neural networks offer an ease of patient-specific parameter
tuning, promising to reduce the burden of programming on clinicians. The paper
explores a compact, feed-forward neural network architecture of only dozens of
units for seizure-state classification in refractory epilepsy. The proposed
classifier offers comparable accuracy to filter classifiers on
clinician-labelled data, while reducing detection latency. As a trade-off to
classical methods, the paper focuses on keeping the complexity of the
architecture minimal, to accommodate the on-board computational constraints of
implantable pulse generator systems.
