论文标题
下一代闭环神经调节疗法的计算有效的神经网络分类器 - 癫痫研究的案例研究
Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy -- a case study in epilepsy
论文作者
论文摘要
这项工作探讨了神经网络分类器在下一代响应式神经调节系统中基于现场的生物标志物实时分类的潜在效用。与经典的基于滤波器的分类器相比，神经网络可轻松使用患者特定的参数调整，有望减轻临床医生的编程负担。该论文探讨了仅在难治性癫痫中进行癫痫发作状态分类的紧凑，前馈神经网络结构。提出的分类器提供了可比的精度，可在临床医生标记的数据上过滤分类器，同时减少检测潜伏期。作为经典方法的权衡，本文着重于保持体系结构的复杂性最小，以适应可植入的脉冲发生器系统的机上计算约束。
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.
