Safety and Tolerability of Burst-Cycling Deep Brain Stimulation for Freezing of Gait in Parkinson's Disease.

Background: Freezing of gait (FOG) is a common symptom in Parkinson's disease (PD) and can be difficult to treat with dopaminergic medications or with deep brain stimulation (DBS). Novel stimulation paradigms have been proposed to address suboptimal responses to conventional DBS programming methods. Burst-cycling deep brain stimulation (BCDBS) delivers current in various frequencies of bursts (e.g., 4, 10, or 15 Hz), while maintaining an intra-burst frequency identical to conventional DBS. Objective: To evaluate the safety and tolerability of BCDBS in PD patients with FOG. Methods: Ten PD subjects with STN or GPi DBS and complaints of FOG were recruited for this single center, single blinded within-subject crossover study. For each subject, we compared 4, 10, and 15 Hz BCDBS to conventional DBS during the PD medication-OFF state. Results: There were no serious adverse events with BCDBS. It was feasible and straightforward to program BCDBS in the clinic setting. The benefit was comparable to conventional DBS in measures of FOG, functional mobility and in PD motor symptoms. BCDBS had lower battery consumption when compared to conventional DBS. Conclusions: BCDBS was feasible, safe and well tolerated and it has potential to be a viable future DBS programming strategy.

Freezing-of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm.

Freezing of Gait (FoG) is an episodic motor disturbance in Parkinson disease (PD) that causes patients to be unable to initiate or maintain their locomotion. Prior work that used simple and easy-to-learn algorithms based on a singular feature and rule-based classifiers are not sufficient to learn variations in patient walking styles and freezing patterns. Efforts to use machine-learning algorithms suffer from challenges caused by imbalanced datasets. Here, we propose a new approach for FoG detection using a wide set of online calculable features and an instance-based and non-parametric Possibilistic K-Nearest-Neighbor (KNN) classifier. The issue of imbalanced datasets is addressed using the Self-Organizing-Map (SOM) algorithm.

Freezing-of-Gait detection using temporal, spatial, and physiological features with a support-vector-machine classifier.

Freezing-of-Gait (FoG) is a syndrome of Parkinson's disease defined by episodes when patients show a complete inability to take a step or continue with their locomotion. In order to develop closed-loop therapeutic strategies, including deep brain stimulation, a reliable means of detecting freezing episodes is required. By using wearable accelerometers, freezing episodes can be detected with energy-based algorithms when the ratio of the energy in the freeze band (3 to 8 Hz) to that of the locomotion band (0.5 to 3 Hz) is above a patient-specific threshold. However, due to the great variability in patient activity type, walking style, and freezing pattern, this detection method often does not work. Here we describe a new FoG-detection method that captures temporal, spatial, and physiological features and uses a support-vector-machine to classify freezing episodes. Since our method uses more diverse features, it is able to more robustly detect FoG events. We have shown that when the energy-based method fails (e.g., area under the receiver operator curve is ~0.5), our new method performs well (e.g., area under ROC curve is 0.96).