Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy
Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy
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DOI：
10.1088/1741-2552/abc740
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发表时间：
2020-12-01
影响因子：
4
通讯作者：
Pequito, Sergio
中科院分区：
文献类型：
--
作者：
Chatterjee, Sarthak;Romero, Orlando;Pequito, Sergio
Objective.电神经刺激是一种越来越多地被采用的治疗方法，用于神经系统疾病，如癫痫。电神经刺激装置通常以其有限的感测、致动和计算能力为特征。然而，感测机构通常仅用于其检测潜力（例如，检测癫痫发作），其自动地和动态地触发致动能力，但最终部署由一段时间的手动（和经验）校准产生的预先指定的刺激剂量。因此，在由感测机构获取的测量中包含的潜在信息相当未被充分利用，因为这种类型的刺激策略仅需要设备的传感器和致动器之间的事件触发关系。这样的刺激策略通常是次优的，并且缺乏关于其性能的理论保证。Approach.为了利用在正常的感测-致动操作期间收集的上述信息，我们必须考虑实时反馈（闭环）策略。更确切地说，刺激信号本身应该基于手头的神经生理系统的状态自动适应，该状态是根据通过设备中的传感器实时收集的数据估计的。主要结果。在这项工作中，我们提出了一种基于模型的方法（实时）闭环电神经刺激，其中系统的演变被捕获的分数阶系统（FOS）。更确切地说，我们提出了一个模型预测控制（MPC）的方法与基础FOS预测模型，由于分数阶动态的能力，更准确地捕捉生物系统中存在的长期依赖性，相比标准的线性时不变模型。此外，MPC通过设计提供了额外的鲁棒性层，以补偿系统模型失配，这是更传统的策略所缺乏的。为了建立我们的框架的潜力，我们专注于癫痫发作缓解的计算模拟我们提出的策略后，癫痫样事件。最后，我们提供证据证明我们的方法对癫痫发作的有效性，这些癫痫发作是由文献中的神经科学和医学界普遍采用的模型模拟的，以及从癫痫受试者获得的真实的癫痫发作数据。因此，我们的研究为强大的实时闭环电神经刺激的开发和实施铺平了道路，然后可以用于构建更有效的癫痫发作缓解装置。
Objective. Electrical neurostimulation is an increasingly adopted therapeutic methodology for neurological conditions such as epilepsy. Electrical neurostimulation devices are commonly characterized by their limited sensing, actuating, and computational capabilities. However, the sensing mechanisms are often used only for their detection potential (e.g. to detect seizures), which automatically and dynamically trigger the actuation capabilities, but ultimately deploy prespecified stimulation doses that resulted from a period of manual (and empirical) calibration. The potential information contained in the measurements acquired by the sensing mechanisms is, therefore, considerably underutilized, given that this type of stimulation strategy only entails an event-triggered relationship between the sensors and actuators of the device. Such stimulation strategies are suboptimal in general and lack theoretical guarantees regarding their performance. Approach. In order to leverage the aforementioned information, harvested during normal sensing-actuating operation, we must consider a real-time feedback (closed-loop) strategy. More precisely, the stimulation signal itself should automatically adapt based upon the state of the neurophysiological system at hand, estimated from data collected in real-time through sensors in the device. Main results. In this work, we propose a model-based approach for (real-time) closed-loop electrical neurostimulation, in which the evolution of the system is captured by a fractional-order system (FOS). More precisely, we propose a model predictive control (MPC) approach with an underlying FOS predictive model, due to the ability of fractional-order dynamics to more accurately capture the long-term dependence present in biological systems, compared to the standard linear time-invariant models. Furthermore, MPC offers, by design, an additional layer of robustness to compensate for system-model mismatch, which the more traditional strategies lack. To establish the potential of our framework, we focus on epileptic seizure mitigation by computational simulation of our proposed strategy upon seizure-like events. Lastly, we provide evidence of the effectiveness of our method on seizures simulated by commonly adopted models in the neuroscience and medical community present in the literature, as well as real seizure data as obtained from subjects with epilepsy. Significance Our study thus paves the way for the development and implementation of robust real-time closed-loop electrical neurostimulation which can then be used for the construction of more effective devices for epileptic seizure mitigation.