论文标题
用于检测术中ECOG中高频振荡（HFO）的尖峰神经网络（SNN）
A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG
论文作者
论文摘要
为了达到癫痫发作，癫痫手术需要完全切除癫痫脑组织。在术中ECOG记录中，癫痫组织产生的高频振荡（HFO）可用于调整切除缘。但是，实时自动检测HFO仍然是一个公开挑战。在这里，我们提出一个用于自动HFO检测的尖峰神经网络（SNN），最适合神经形态硬件实现。我们训练了SNN，以使用独立标记的数据集检测从术中ECOG测量的HFO信号。我们针对快速波纹频率范围（250-500 Hz）的HFO的检测，并将网络结果与标记的HFO数据进行了比较。我们将SNN具有一种新型的伪影排斥机制，以抑制尖锐的瞬变并证明其在ECOG数据集上的有效性。该SNN检测到的HFO率（中位数为6.6 HFO/min）与数据集（58分钟，16个记录）相当。所有8例患者的术后癫痫发作结果被“预测”为100％的精度。这些结果为构建实时便携式电池供电的HFO检测系统提供了进一步的一步，该检测系统可在癫痫手术期间使用，以指导癫痫发作区的切除。
To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative ECoG recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset. We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (58 min, 16 recordings). The postsurgical seizure outcome was "predicted" with 100% accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.
