Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy – a case study in epilepsy

• DOI: 10.48550/arxiv.2204.12938
• Publication Date: 2022-07-11
• Authors: Kavoosi, A; Toth, R; Benjaber, M; Zamora, M; Valentin, A; Sharott, A; Denison, T
• Keywords: Signal Processing (eess.SP); Drug Resistant Epilepsy; Epilepsy; 610; Quantitative Biology - Quantitative Methods; Article; 03 medical and health sciences; 0302 clinical medicine; Seizures; FOS: Biological sciences; FOS: Electrical engineering, electronic engineering, information engineering; Humans; Neural Networks, Computer; Electrical Engineering and Systems Science - Signal Processing; Quantitative Methods (q-bio.QM)

4 pages, 5 figures<br/>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.<br/>