Faculdade de Engenharia, Universidade do Porto, Portugal.
Faculdade de Engenharia, Universidade do Porto, Portugal.
Gait Posture. 2022 Oct;98:49-55. doi: 10.1016/j.gaitpost.2022.08.014. Epub 2022 Aug 20.
Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.
This article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.
Gait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.
In the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.
The results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.
帕金森病（PD）是一种慢性进行性神经退行性疾病，目前尚无治愈方法，其诊断和管理具有挑战性。然而，尽管已经提出了大量的标准和指南来改善 PD 的诊断并确定 PD 分期，但 PD 的诊断和症状监测的金标准仍然主要基于临床评估，其中包括一些主观因素。使用机器学习（ML）算法分析时空步态参数是一个有趣的进展，具有易于解释和客观因素，可能有助于 PD 的诊断和随访。
本文研究了 ML 算法，以：i）区分 PD 患者与匹配的健康个体；ii）基于选定的时空参数，包括变异性和不对称性，区分 PD 阶段。
在实验中，研究了 63 名不同 PD 运动症状严重程度的 PD 患者和 63 名匹配对照组个体在自主选择的步行速度下采集的步态数据。
在 PD 诊断中，朴素贝叶斯算法的分类准确率为 84.6%，精度为 0.923，召回率为 0.800。我们在 PD 诊断中发现了四个显著的步态特征：步长、速度和宽度以及步宽变异性。对于 PD 分期的识别，随机森林算法优于其他研究的 ML 算法，达到了 0.786 的 ROC 曲线下面积。我们发现了两个与识别 PD 阶段相关的步态特征：步宽变异性和步双支撑时间变异性。
结果表明，所研究的 ML 算法通过分析步态参数具有 PD 诊断和分期识别的潜力。
