基于计算模型的自适应植入式神经刺激器全闭环测试环境。

IF 0.8 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Scott Stanslaski, Hafsa Farooqi, David Escobar Sanabria, Theoden Ivan Netoff
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引用次数: 4

摘要

植入式脑刺激装置继续发展,以治疗和监测大脑状况。随着这些设备的复杂性增加,包括适应性神经调节治疗,验证操作和验证这些系统的正确性变得更加复杂。新的复杂性在于设备的功能取决于与患者的相互作用和环境因素,如噪音和伪影。在这里,我们提出了一个硬件在环(HIL)测试框架,该框架采用病理神经动力学计算模型来测试自适应深部脑刺激(DBS)设备,然后进行动物或人体测试。大脑刺激和记录电极阵列被放置在生理盐水槽中,并连接到一个自适应神经调节系统,该系统测量和处理合成信号并将刺激传递回生理盐水槽。数据采集系统用于检测刺激并向计算模型提供反馈,以模拟刺激对神经动力学的影响。在这项研究中,我们使用实时计算模型模拟癫痫患者丘脑前核(ANT)的癫痫发作动态和帕金森病(PD)患者丘脑下核(STN)的β波段(11-35 Hz)振荡。这些模型模拟了神经元对电刺激脉冲的反应,盐水罐测试了检测算法和刺激干扰之间的硬件相互作用。我们测试并验证了嵌入在植入式DBS系统(Medtronic Summit RC+S)中的用于癫痫发作和β波段功率抑制的自适应DBS算法的操作。本研究强调了所提出的硬件在环框架在系统干扰(如环境噪声和刺激诱发的电子伪影)存在下系统测试自适应DBS系统的实用性。该测试程序可以帮助确保自适应DBS设备在动物和人体测试之前的正确性和稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models.

Implantable brain stimulation devices continue to be developed to treat and monitor brain conditions. As the complexity of these devices grows to include adaptive neuromodulation therapy, validating the operation and verifying the correctness of these systems becomes more complicated. The new complexities lie in the functioning of the device being dependent on the interaction with the patient and environmental factors such as noise and artifacts. Here, we present a hardware-in-the-loop (HIL) testing framework that employs computational models of pathological neural dynamics to test adaptive deep brain stimulation (DBS) devices prior to animal or human testing. A brain stimulation and recording electrode array is placed in the saline tank and connected to an adaptive neuromodulation system that measures and processes the synthetic signals and delivers stimulation back into the saline tank. A data acquisition system is used to detect the stimulation and provide feedback to the computational model in order to simulate the effects of stimulation on the neural dynamics. In this study, we used real-time computational models to emulate the dynamics of epileptic seizures observed in the anterior nucleus of the thalamus (ANT) in epilepsy patients and beta band (11-35 Hz) oscillations observed in the subthalamic nucleus (STN) of Parkinson's disease (PD) patients. These models simulated neuronal responses to electrical stimulation pulses and the saline tank tested hardware interactions between the detection algorithms and stimulation interference. We tested and validated the operation of adaptive DBS algorithms for seizure and beta band power suppression embedded in an implantable DBS system (Medtronic Summit RC+S). This study highlights the utility of the proposed hardware-in-the-loop framework to systematically test the adaptive DBS systems in the presence of system aggressors such as environmental noise and stimulation-induced electrical artifacts. This testing procedure can help ensure correctness and robustness of adaptive DBS devices prior to animal and human testing.

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来源期刊
CiteScore
1.80
自引率
11.10%
发文量
56
审稿时长
6-12 weeks
期刊介绍: The Journal of Medical Devices presents papers on medical devices that improve diagnostic, interventional and therapeutic treatments focusing on applied research and the development of new medical devices or instrumentation. It provides special coverage of novel devices that allow new surgical strategies, new methods of drug delivery, or possible reductions in the complexity, cost, or adverse results of health care. The Design Innovation category features papers focusing on novel devices, including papers with limited clinical or engineering results. The Medical Device News section provides coverage of advances, trends, and events.
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