A machine-learning approach to volitional control of a closed-loop deep brain stimulation system
A machine-learning approach to volitional control of a closed-loop deep brain stimulation system
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DOI：
10.1088/1741-2552/aae67f
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发表时间：
2019-02-01
影响因子：
4
通讯作者：
Chizeck, Howard
中科院分区：
文献类型：
--
作者：
Houston, Brady;Thompson, Margaret;Chizeck, Howard
Objective.脑深部电刺激（DBS）是治疗原发性震颤的一种行之有效的治疗方法，但可能不是最佳治疗方法，因为无论症状如何，它都是持续的。闭环（CL）DBS使用生物信号来确定何时应该给予刺激，可能会更好。皮质活动是一种很有前途的生物信号，可用于闭环系统，因为它包含与病理和正常运动相关的特征。然而，神经信号在个体之间是不同的，这使得很难创建一个“一刀切”的闭环系统。Approach.我们使用机器学习来创建患者特定的CL DBS系统。在该系统中，二进制分类器用于从皮层信号中提取患者特异性特征，并确定何时发生意志性震颤诱发运动以真实的改变刺激电压。主要结果。该系统能够在受试者移动的87%-100%的时间内提供刺激。此外，我们表明，该系统的治疗效果至少与当前的连续刺激范例一样好。意义这些发现证明了CL DBS治疗的前景，并强调了在这些系统中使用受试者特定模型的重要性。
Objective. Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation should be given, may be better. Cortical activity is a promising biosignal for use in a closed-loop system because it contains features that are correlated with pathological and normal movements. However, neural signals are different across individuals, making it difficult to create a 'one size fits all' closed-loop system. Approach. We used machine learning to create a patient-specific, CL DBS system. In this system, binary classifiers are used to extract patient-specific features from cortical signals and determine when volitional, tremor-evoking movement is occurring to alter stimulation voltage in real time. Main results. This system is able to deliver stimulation up to 87%-100% of the time that subjects are moving. Additionally, we show that the therapeutic effect of the system is at least as good as that of current, continuous-stimulation paradigms. Significance. These findings demonstrate the promise of CL DBS therapy and highlight the importance of using subject-specific models in these systems.