Deep Brain Stimulation of the Subthalamic Nucleus

The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

“…The most popular approach has been to use a frequency-based analysis of leg accelerations to capture the "trembling of knees" associated with FOG, and many variations on this approach have been described including the "freeze index" [13] and "Frequency Ratio" [14]. These studies have employed a variety of different FOG-eliciting tasks, such as turning 360 degrees in place for two minutes, walking around cones, or walking during dual tasking [14][15][16][17][18][19][20][21][22]. These tasks have improved the detection of FOG but are not representative of real-world environments, or cannot objectively measure gait arrhythmicity, which has been correlated with FOG [23][24][25][26][27].…”

The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

“…Abbreviations FOG = freezing of gait; DBS = deep brain stimulation; STN = subthalamic nucleus; TBC = Turning and Barrier Course; FW = forward walking; Inertial Measurement Unit = IMU; FOG-Q = Freezing of Gait Questionnaire; LOOCV = leave-one-out cross validation; AUROC = Area Under Receiver Operator Curve; UPDRS = Unified Parkinson's Disease Rating Scale assessments, and clinic-based measurements (Barthel et al, 2016). The authors concluded that there is no "unique methodological tool that encompasses the entire complexity of FOG" and "further development of such an assessment tool requires understanding and thorough analysis of the specific FOG characteristics" (Barthel et al, 2016).Several studies have employed wearable inertial sensors in a variety of different tasks, such as turning 360 degrees in place for two minutes, walking around cones, or walking during dual tasking, to monitor, detect and predict FOG (Coste et al, 2014;Khemani et al, 2015;Kim et al, 2015;Kwon et al, 2014;Palmerini et al, 2017;Rezvanian and Lockhart, 2016;Silva de Lima et al, 2017;Zach et al, 2015). These tasks have improved the detection resolution of FOG but are either not representative of real-world environments or still require a clinical rater to detect freezing episodes, and cannot objectively measure gait impairment, such as arrhythmicity, that is correlated with FOG (Anidi et al, 2018;Hausdorff, 2009;Nantel et al, 2011;Plotnik and Hausdorff, 2008;Syrkin-Nikolau et al, 2017).…”

“…Several studies have employed wearable inertial sensors in a variety of different tasks, such as turning 360 degrees in place for two minutes, walking around cones, or walking during dual tasking, to monitor, detect and predict FOG (Coste et al, 2014;Khemani et al, 2015;Kim et al, 2015;Kwon et al, 2014;Palmerini et al, 2017;Rezvanian and Lockhart, 2016;Silva de Lima et al, 2017;Zach et al, 2015). These tasks have improved the detection resolution of FOG but are either not representative of real-world environments or still require a clinical rater to detect freezing episodes, and cannot objectively measure gait impairment, such as arrhythmicity, that is correlated with FOG (Anidi et al, 2018;Hausdorff, 2009;Nantel et al, 2011;Plotnik and Hausdorff, 2008;Syrkin-Nikolau et al, 2017).…”