用于颅内EEG高频振荡（HFO）实时检测的电子神经形态学系统
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
摘要：
在这项工作中，我们提出了一种神经形态系统，该系统首次将神经记录头台与信号-尖峰转换电路和多核尖峰神经网络（SNN）架构结合在同一个模具上，用于记录，处理和检测高频振荡（HFO），这是致痫区的生物标志物。该器件采用标准的0.18 $μ$ m CMOS工艺节点制造，总面积为99 mm $^{2}$。我们证明了它的应用程序HFO检测记录从9例颞叶癫痫患者随后接受癫痫手术的iEEG。芯片在检测任务期间的总平均功耗为614.3 $μ$ W。我们展示了神经形态学系统如何可靠地检测HFO：该系统以最先进的准确性，特异性和灵敏度（分别为78%，100%和33%）预测术后癫痫发作结果。这是第一个可行性研究，旨在使用基于事件的处理器和尖峰神经网络实时、片上识别颅内人体数据中的相关特征。通过向神经记录电路提供“神经形态智能”，所提出的方法将为开发能够直接在手术室中检测HFO区域并改善癫痫手术的癫痫发作结果的系统铺平道路。
英文摘要：
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$μ$m CMOS technology node and has a total area of 99mm$^{2}$. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3$μ$W. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing "neuromorphic intelligence" to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.
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