Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like events, also providing insights to improve the understanding of the neural dynamics in the interictal and ictal periods, although the physics behind each parameter and variable of the model is not fully established in the literature. This paper introduces an approach to design a feedback-based controller for suppressing epileptic seizures described by Epileptor. Our work establishes how the nonlinear dynamics of this disorder can be written in terms of a combination of linear sub-models employing an exact solution. Additionally, we show how a feedback control gain can be computed to suppress seizures, as well as how specific shapes applied as input stimuli for this purpose can be obtained. The practical application of the approach is discussed and the results show that the proposed technique is promising for developing controllers in this field.

中文翻译：

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定义输入刺激以抑制癫痫模型描述的癫痫发作的有效方法

癫痫影响全球约 7000 万人。每年，大约有 240 万人被诊断出患有癫痫症，其中三分之二的人不知道其疾病的病因，其中 1% 的人会因此而死亡。由于预测和解释它的固有复杂性，最近开发了数学模型 Epileptor 来重现癫痫样事件，也提供见解以提高对发作间期和发作期神经动力学的理解，尽管每个参数背后的物理原理和该模型的变量在文献中并未完全确立。本文介绍了一种设计基于反馈的控制器来抑制 Epileptor 描述的癫痫发作的方法。我们的工作确定了如何根据采用精确解的线性子模型的组合来编写这种无序的非线性动力学。此外，我们展示了如何计算反馈控制增益以抑制癫痫发作，以及如何获得为此目的用作输入刺激的特定形状。讨论了该方法的实际应用，结果表明，所提出的技术有望用于开发该领域的控制器。