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$\mu$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$\mu$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.

中文翻译：

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用于实时检测颅内 EEG 中高频振荡 (HFO) 的电子神经形态系统

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探头与信号到尖峰转换电路和多核尖峰神经网络 (SNN) 架构结合在同一芯片上，用于记录、处理和检测高频振荡 (HFO)，这是致癫痫区的生物标志物。该器件使用标准的 0.18$\mu$m CMOS 技术节点制造，总面积为 99mm$^{2}$。我们证明了其在 9 名颞叶癫痫患者的 iEEG 中 HFO 检测中的应用，这些患者随后接受了癫痫手术。芯片在检测任务期间的总平均功耗为 614.3$\mu$W。我们展示了神经形态系统如何可靠地检测 HFO：该系统以最先进的精度预测术后癫痫发作结果，特异性和敏感性（分别为 78%、100% 和 33%）。这是使用基于事件的处理器和尖峰神经网络实时、在芯片上识别颅内人类数据中相关特征的第一个可行性研究。通过为神经记录电路提供“神经形态智能”，所提出的方法将为开发可以直接在手术室中检测 HFO 区域的系统铺平道路，并改善癫痫手术的癫痫发作结果。