Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, 100124 Beijing, China.
Department of Dynamics and Control, Beihang University, 100191 Beijing, China.
Chaos. 2020 Oct;30(10):103114. doi: 10.1063/5.0021693.
Given the complex temporal evolution of epileptic seizures, understanding their dynamic nature might be beneficial for clinical diagnosis and treatment. Yet, the mechanisms behind, for instance, the onset of seizures are still unknown. According to an existing classification, two basic types of dynamic onset patterns plus a number of more complex onset waveforms can be distinguished. Here, we introduce a basic three-variable model with two time scales to study potential mechanisms of spontaneous seizure onset. We expand the model to demonstrate how coupling of oscillators leads to more complex seizure onset waveforms. Finally, we test the response to pulse perturbation as a potential biomarker of interictal changes.
鉴于癫痫发作的复杂时间演变,了解其动态特性可能对临床诊断和治疗有益。然而,例如,癫痫发作的机制仍不清楚。根据现有的分类,可以区分两种基本类型的动态发作模式和一些更复杂的发作波形。在这里,我们引入一个具有两个时间尺度的基本三变量模型来研究自发性癫痫发作的潜在机制。我们扩展模型以展示振荡器的耦合如何导致更复杂的发作起始波形。最后,我们测试对脉冲干扰的响应作为发作间期变化的潜在生物标志物。