Demonstration of Kinematic-Based Closed-loop Deep Brain Stimulation for Mitigating Freezing of Gait in People with Parkinson's Disease.
Demonstration of Kinematic-Based Closed-loop Deep Brain Stimulation for Mitigating Freezing of Gait in People with Parkinson's Disease.
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DOI：
10.1109/embc44109.2020.9176638
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Bronte-Stewart HM
中科院分区：
文献类型：
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作者：
O'Day JJ;Kehnemouyi YM;Petrucci MN;Anderson RW;Herron JA;Bronte-Stewart HM
帕金森氏病中的步态受损的特征是缓慢的、步态不稳的踏步，并且通常包括其中交替踏步完全停止的步态发作的冻结。可穿戴惯性传感器提供了一种检测这些步态变化的方法，新型脑深部电刺激（DBS）系统可以实时闭环方式响应临床治疗。在本文中，我们提出了两个新的闭环DBS算法，一个使用步态的稳定性和一个使用逻辑回归模型的步态检测冻结作为控制信号。实验室验证结果表明，通过响应实时人类受试者运动学数据和来自帕金森病参与者的腿戴惯性传感器的预记录数据，结合闭环DBS系统运行这些算法的可行性。我们还提出了一种新的控制策略算法，改变神经刺激器的频率响应的运动学输入。这些结果为进一步开发、迭代和在临床试验中测试第一个闭环DBS算法提供了基础，该算法使用运动学信号来治疗性地改善和理解帕金森病步态障碍的病理生理机制。
Impaired gait in Parkinson’s disease is marked by slow, arrhythmic stepping, and often includes freezing of gait episodes where alternating stepping halts completely. Wearable inertial sensors offer a way to detect these gait changes and novel deep brain stimulation (DBS) systems can respond with clinical therapy in a real-time, closed-loop fashion. In this paper, we present two novel closed-loop DBS algorithms, one using gait arrhythmicity and one using a logistic-regression model of freezing of gait detection as control signals. Benchtop validation results demonstrate the feasibility of running these algorithms in conjunction with a closed-loop DBS system by responding to real-time human subject kinematic data and pre-recorded data from leg-worn inertial sensors from a participant with Parkinson’s disease. We also present a novel control policy algorithm that changes neurostimulator frequency in response to the kinematic inputs. These results provide a foundation for further development, iteration, and testing in a clinical trial for the first closed-loop DBS algorithms using kinematic signals to therapeutically improve and understand the pathophysiological mechanisms of gait impairment in Parkinson’s disease.