Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Nat Commun. 2021 May 25;12(1):3095. doi: 10.1038/s41467-021-23342-2.
The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.
生物医学信号的分析对于临床研究和治疗应用有很大的帮助，而这可以通过能实时、就地处理这些信号的嵌入式设备来实现。例如，对癫痫患者颅内脑电图（iEEG）的分析可用于检测高频振荡（HFO），HFO 是致痫性脑组织的生物标志物。混合信号神经形态电路提供了构建紧凑、低功耗神经网络处理系统的可能性，该系统可实时在线分析数据。在这里，我们提出了一种神经形态系统，该系统将神经记录前端与尖峰神经网络（SNN）处理核心集成在同一芯片上，用于处理 iEEG，并展示了它如何可靠地检测 HFO，从而实现了最先进的准确性、灵敏度和特异性。这是使用混合信号神经形态计算技术实时识别 iEEG 中相关特征的首次可行性研究。
