Department of Information Engineering, Binzhou University, Binzhou 256600, P. R. China.
Int J Neural Syst. 2020 Jan;30(1):1950023. doi: 10.1142/S0129065719500230. Epub 2019 Aug 2.
Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.
癫痫发作是由多个空间分离的神经群同步放电引起的；因此，许多同步性度量被用于癫痫发作的检测和特征描述。然而，同步性度量仅反映神经元群体之间的整体相互作用强度，但不能揭示个体群体之间的耦合强度，这对于癫痫控制更为重要。可达性和可达聚类的概念被提出，以表示一组神经群的耦合强度。在这里，我们描述了一种基于耦合强度的癫痫控制方法，使用组合卷积神经网络（CCNN）建模。基于神经生理学的神经群模型（NMM）可以将信号处理和神经生理学联系起来，用于模拟所提出的控制器。尽管邻接矩阵和可达矩阵不能被完美识别，但使用具有最佳阈值的 CCNN，绝大多数邻接值被识别，达到 95.64%。对于离散和连续的耦合强度情况，所提出的控制器将平均可达聚类强度保持在约 0.1，表明可以有效地控制癫痫发作。
