O'Day Johanna J, Kehnemouyi Yasmine M, Petrucci Matthew N, Anderson Ross W, Herron Jeffrey A, Bronte-Stewart Helen M
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3612-3616. doi: 10.1109/EMBC44109.2020.9176638.
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.
帕金森病患者的步态障碍表现为缓慢、无节律的行走，且常伴有步态冻结发作，即交替行走完全停止。可穿戴惯性传感器提供了一种检测这些步态变化的方法，新型深部脑刺激（DBS）系统能够以实时闭环方式进行临床治疗。在本文中，我们提出了两种新型闭环DBS算法，一种使用步态无节律性，另一种使用步态冻结检测的逻辑回归模型作为控制信号。台式验证结果表明，通过响应帕金森病患者的实时人体运动学数据和腿部佩戴的惯性传感器的预记录数据，将这些算法与闭环DBS系统结合运行是可行的。我们还提出了一种新型控制策略算法，该算法可根据运动学输入改变神经刺激器频率。这些结果为首次使用运动学信号进行治疗性改善和理解帕金森病步态障碍病理生理机制的闭环DBS算法在临床试验中的进一步开发、迭代和测试奠定了基础。
