Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.
Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.
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DOI：
10.3390/s23198330
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发表时间：
2023-10-09
期刊：
影响因子：
--
通讯作者：
Kwon H
中科院分区：
文献类型：
--
作者：
Gong NJ;Clifford GD;Esper CD;Factor SA;McKay JL;Kwon H
表征帕金森病（PD）的运动亚型是临床护理的一个重要方面，有助于预后和医疗管理。尽管所有PD病例都涉及大脑中多巴胺能神经元的丧失，但个别病例可能表现出不同的运动体征组合，这可能表明潜在病理和对治疗的潜在反应存在差异。然而，区分PD运动亚型的传统方法需要由运动障碍专家进行资源密集的体格检查。此外，PD的标准化评定量表依赖于主观观察，这需要专门的培训和不可避免的评分者之间的差异。在这项工作中，我们提出了一个系统，该系统使用机器学习模型，从PD患者步行任务中记录的3D运动学数据（MDS-UPDRS-III评分，34.7±10.5，平均疾病持续时间7.5±4.5年）中自动客观地识别一些PD运动亚型，特别是震颤主导型（TD）和姿势不稳定和步态困难（PIGD）。本研究展示了一种利用运动学数据的机器学习模型，该模型识别PD运动亚型的F1评分为79.6% （N = 55名帕金森病患者）。这明显优于基于步态特征分类的比较模型（F1得分为19.8%）。我们的模型对个体患者进行训练的变体获得了95.4%的F1得分。该分析显示，下体运动的时间、频谱和统计特征有助于区分运动亚型。简单地从步行中自动评估PD运动亚型可以减少专家所需的时间和资源，从而改善PD治疗的患者护理。此外，该系统可以提供客观的评估，跟踪PD运动亚型随时间的变化，以便根据需要实施和修改个别患者的适当治疗计划。
Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.
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