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Bifurcation analysis of the Poincare map function of intracranial EEG signals in temporal lobe epilepsy patients

机译:颞叶癫痫患者颅内脑电信号Poincare图功能的分叉分析

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In this paper, the Poincare map function as a one-dimensional first-return map is obtained by approximating the scatter plots of inter-peak interval (IPI) during preictal and postictal periods from invasive EEG recordings of nine patients suffering from medically intractable focal epilepsy. Evolutionary Algorithm (EA) is utilized for parameter estimation of the Poincare' map. Bifurcation analyses of the iterated map reveal that as the neuronal activity progresses from preictal state toward the ictal event, the parameter values of the Poincard map move toward the bifurcation points. However, following the seizure occurrence and in the postictal period, these parameter values move away from the bifurcation points. Both flip and fold bifurcations are analyzed and it is demonstrated that in some cases the flip bifurcation and in other cases the fold bifurcation are the dynamical regime underlying epileptiform events. This information can offer insights into the dynamical nature and variability of the brain signals and consequently could help to predict and control seizure events.
机译:本文通过对9名患有医学顽固性局灶性癫痫患者的有创脑电图进行记录,通过近似峰前和峰后时期峰间间隔(IPI)的散点图来获得Poincare映射作为一维首次返回映射的功能。进化算法(EA)用于Poincare图的参数估计。迭代图的分叉分析表明,随着神经元活动从发作状态向发作事件发展,庞加德图的参数值也向分叉点移动。但是,在癫痫发作发生后和发作后的这段时间内,这些参数值会偏离分叉点。分析了翻转分叉和折叠分叉,并证明了在某些情况下翻转分叉和在其他情况下折叠分叉是癫痫样事件背后的动力学机制。该信息可以提供有关脑信号的动态性质和可变性的见解,因此可以帮助预测和控制癫痫发作。

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