Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K
Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran,
J Comput Neurosci. 2014 Feb;36(1):39-53. doi: 10.1007/s10827-013-0457-5. Epub 2013 Jun 4.
By assuming the brain as a multi-stable system, different scenarios have been introduced for transition from normal to epileptic state. But, the path through which this transition occurs is under debate. In this paper a stochastic model for seizure genesis is presented that is consistent with all scenarios: a two-level spontaneous seizure generation model is proposed in which, in its first level the behavior of physiological parameters is modeled with a stochastic process. The focus is on some physiological parameters that are essential in simulating different activities of ElectroEncephaloGram (EEG), i.e., excitatory and inhibitory synaptic gains of neuronal populations. There are many depth-EEG models in which excitatory and inhibitory synaptic gains are the adjustable parameters. Using one of these models at the second level, our proposed seizure generator is complete. The suggested stochastic model of first level is a hidden Markov process whose transition matrices are obtained through analyzing the real parameter sequences of a seizure onset area. These real parameter sequences are estimated from real depth-EEG signals via applying a parameter identification algorithm. In this paper both short-term and long-term validations of the proposed model are done. The long-term synthetic depth-EEG signals simulated by this model can be taken as a suitable tool for comparing different seizure prediction algorithms.
通过将大脑假设为一个多稳态系统，已经引入了从正常状态转变为癫痫状态的不同情形。但是，这种转变发生的途径仍存在争议。本文提出了一种与所有情形都一致的癫痫发作产生的随机模型：提出了一个两级自发癫痫发作生成模型，在其第一级中，生理参数的行为用一个随机过程进行建模。重点关注一些在模拟脑电图（EEG）不同活动中至关重要的生理参数，即神经元群体的兴奋性和抑制性突触增益。有许多深度脑电图模型，其中兴奋性和抑制性突触增益是可调参数。在第二级使用这些模型中的一个，我们提出的癫痫发作发生器就完整了。所建议的第一级随机模型是一个隐马尔可夫过程，其转移矩阵是通过分析癫痫发作起始区域的实际参数序列获得的。这些实际参数序列是通过应用参数识别算法从实际深度脑电图信号中估计出来的。本文对所提出的模型进行了短期和长期验证。由该模型模拟的长期合成深度脑电图信号可作为比较不同癫痫发作预测算法的合适工具。
