Data-Driven Prediction of Freezing of Gait Events From Stepping Data.
Data-Driven Prediction of Freezing of Gait Events From Stepping Data.
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步态数据中步态事件冻结的数据驱动预测。
DOI：
10.3389/fmedt.2020.581264
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发表时间：
2020
影响因子：
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通讯作者：
Tsaneva-Atanasova K
中科院分区：
文献类型：
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作者：
Parakkal Unni M;Menon PP;Livi L;Wilson MR;Young WR;Bronte-Stewart HM;Tsaneva-Atanasova K
步态冻结（FoG）是晚期帕金森病（PD）的典型症状，会对生活质量产生负面影响，并且通常对药物干预具有抗性。利用听觉或感官线索的新治疗方案可能通过预测冻结事件而得到优化。这些预测可能有助于触发外部感官线索——当行为以一种指示即将冻结的方式改变时（即，当用户最需要它的时候），而不是连续地传递线索信息。提出了一种数据驱动的方法，用于使用随机福雷斯特（RF）、神经网络（NN）和朴素贝叶斯（NB）分类器预测冻结事件。从9名PD受试者身上收集垂直力，采样频率为100 Hz，当他们站在原地，直到他们至少有一次冻结发作或持续90秒。计算了不同IL（机器学习算法的输入）和GL（预测冻结事件的时间）下RF/NN/NB算法的F1分数。F1评分与GL呈显著负相关，突出了早期发现的困难。使F1分数最大化的IL大约等于1.13 s。这表明，导致冻结的生理（因此也是神经学）变化至少在冻结事件发生前一步就开始起作用了。我们的算法有潜力支持设备的开发，以检测并潜在地防止帕金森病患者的冻结事件，如果不加以纠正，可能会发生。
Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues—shown to improve walking performance—when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD subjects as they stepped in place until they at least had one freezing episode or for 90 s. The F1 scores of RF/NN/NB algorithms were computed for different IL (input to the machine learning algorithm), and GL (how early the freezing event is predicted). A significant negative correlation between the F1 scores and GL, highlighting the difficulty of early detection is found. The IL that maximized the F1 score is approximately equal to 1.13 s. This indicates that the physiological (and therefore neurological) changes leading to freezing take effect at-least one step before the freezing incident. Our algorithm has the potential to support the development of devices to detect and then potentially prevent freezing events in people with Parkinson's which might occur if left uncorrected.