Sahin Gürdal, Halje Pär, Uzun Sena, Jakobsson Andreas, Petersson Per
Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Department of Internal Medicine, Hässleholm Hospital, Region Skåne, Hässleholm, Sweden.
Front Neurosci. 2022 Aug 1;16:861668. doi: 10.3389/fnins.2022.861668. eCollection 2022.
Tremor can be highly incapacitating in everyday life and typically fluctuates depending on motor state, medication status as well as external factors. For tremor patients being treated with deep-brain stimulation (DBS), adapting the intensity and pattern of stimulation according the current needs therefore has the potential to generate better symptomatic relief. We here describe a procedure for how patients independently could perform self-tests in their home to generate sensor data for on-line adjustments of DBS parameters. Importantly, the inertia sensor technology needed exists in any standard smartphone, making the procedure widely accessible. Applying this procedure, we have characterized detailed features of tremor patterns displayed by both Parkinson's disease and essential tremor patients and directly compared measured data against both clinical ratings (Fahn-Tolosa-Marin) and finger-attached inertia sensors. Our results suggest that smartphone accelerometry, when used in a standardized testing procedure, can provide tremor descriptors that are sufficiently detailed and reliable to be used for closed-loop control of DBS.
震颤在日常生活中可能会严重影响生活能力，并且通常会根据运动状态、用药情况以及外部因素而波动。对于接受深部脑刺激（DBS）治疗的震颤患者，根据当前需求调整刺激强度和模式因此有可能产生更好的症状缓解效果。我们在此描述一种程序，患者可以在家中独立进行自我测试，以生成用于在线调整DBS参数的传感器数据。重要的是，所需的惯性传感器技术存在于任何标准智能手机中，使得该程序广泛可用。应用此程序，我们已经表征了帕金森病和特发性震颤患者所显示的震颤模式的详细特征，并将测量数据与临床评分（法恩 - 托洛萨 - 马林）和手指附着的惯性传感器直接进行了比较。我们的结果表明，当在标准化测试程序中使用时，智能手机加速度计可以提供足够详细和可靠的震颤描述符，用于DBS的闭环控制。
