IEEE Trans Neural Syst Rehabil Eng. 2018 Aug;26(8):1618-1625. doi: 10.1109/TNSRE.2018.2852222. Epub 2018 Jul 2.
Deep brain stimulation (DBS) programming, the systematic selection of fixed electrical stimulation parameters that deliver maximal therapeutic benefit while limiting side effects, poses several challenges in the treatment of movement disorders. DBS programming requires the expertise of trained neurologists or nurses who assess patient symptoms according to standardized clinical rating scales and use patient reports of DBS-related side effects to adjust stimulation parameters and optimize therapy. In this paper, we describe and validate an automated software platform for DBS programming for tremor associated with Parkinson's disease and essential tremor. DBS parameters are changed automatically through a direct computer interface with implanted neurostimulators. Each tested DBS setting is ranked according to its effect on tremor, which is assessed using smartwatch inertial measurement unit data, and side effects, which are reported through a user interface. Blinded neurologist assessments showed the automated programming method performed at least as well as clinician mediated programming in selecting the optimal settings for tremor therapy. This proof of concept study describes a novel DBS programming paradigm that may improve programming efficiency and outcomes, increase access to programming outside specialty clinics, and aid in the development of adaptive and closed-loop DBS strategies.
深部脑刺激 (DBS) 编程是指系统地选择固定的电刺激参数,以在限制副作用的同时提供最大的治疗效果,这在治疗运动障碍方面带来了一些挑战。DBS 编程需要经过培训的神经科医生或护士的专业知识,他们根据标准化的临床评分量表评估患者的症状,并使用患者对 DBS 相关副作用的报告来调整刺激参数和优化治疗。在本文中,我们描述并验证了一种用于治疗帕金森病和原发性震颤相关震颤的自动 DBS 编程软件平台。通过与植入式神经刺激器的直接计算机接口,自动更改 DBS 参数。根据智能手表惯性测量单元数据评估的震颤效果以及通过用户界面报告的副作用,对每个测试的 DBS 设置进行排名。盲法神经科医生评估表明,自动编程方法在选择最佳震颤治疗设置方面至少与临床医生介导的编程一样有效。这项概念验证研究描述了一种新的 DBS 编程范例,可能会提高编程效率和结果,增加在专业诊所之外进行编程的机会,并有助于开发自适应和闭环 DBS 策略。