Watts Jeremy, Niethammer Martin, Khojandi Anahita, Ramdhani Ritesh
Department of Mathematics, University of Tennessee, Knoxville, TN, United States.
Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
Front Aging Neurosci. 2024 Jun 25;16:1431280. doi: 10.3389/fnagi.2024.1431280. eCollection 2024.
Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments.
This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication-OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation.
A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance ( < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle.
These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification.
冻结步态（FOG）是一种阵发性运动现象，随着帕金森病（PD）的进展，其患病率会增加。它与该人群生活质量下降和跌倒风险增加有关。基于运动学评估对冻结步态患者进行精准检测和分类对于制定个性化治疗方案至关重要。
本研究分析了接受丘脑底核脑深部电刺激（STN-DBS）的晚期PD患者的仪器化站立和行走（SAW）试验。每位患者在未服药-未进行DBS刺激状态下进行两次SAW试验。对于每次试验，通过机器学习分类算法分析可穿戴传感器的步态汇总统计数据。这些算法包括k近邻算法、逻辑回归、朴素贝叶斯、随机森林和支持向量机（SVM）。选择这些模型是因其具有较高的可解释性。每项算法的任务是将经训练的运动障碍专家评估的SAW试验中MDS-UPDRS FOG子评分非零的患者进行分类。使用分层五折交叉验证评估这些算法的性能。
共评估了21名PD受试者（平均年龄64.24岁，16名男性，平均病程14年）。14名受试者在未服药/未进行DBS刺激时出现冻结步态。所有机器学习模型均具有统计学上相似的预测性能（<0.05）且准确率较高。对随机森林的特征估计分析揭示了模型中使用的前十种时空预测特征：足跟着地角度、冠状面运动范围[躯干和腰椎]、步长、步态速度、侧向步幅变异性和足趾离地角度。
这些结果表明，机器学习能够基于非药物/非刺激状态下的SAW试验有效地将晚期PD患者分类为冻结步态患者或非冻结步态患者。机器学习模型，特别是随机森林，不仅依赖于显著的时空步态特征进行FOG分类，还利用这些特征进行分类。
