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.
@article{arxiv.2204.12938,
title = {Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy -- a case study in epilepsy},
author = {Ali Kavoosi and Robert Toth and Moaad Benjaber and Mayela Zamora and Antonio Valentin and Andrew Sharott and Timothy Denison},
journal= {arXiv preprint arXiv:2204.12938},
year = {2023}
}