Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device.
Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device.
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DOI：
10.3390/s17092067
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发表时间：
2017-09-09
期刊：
影响因子：
--
通讯作者：
Park KS
中科院分区：
文献类型：
--
作者：
Jeon H;Lee W;Park H;Lee HJ;Kim SK;Kim HB;Jeon B;Park KS
尽管临床上需要能够准确测量帕金森震颤的新技术，但尚未采用使用机器学习方法对帕金森震颤进行自动评分。这项研究旨在通过提出机器学习算法来填补这一空白，作为预测统一帕金森病评定量表（Unified Parkinson's Disease Rating Scale，简称EADS）的一种方法，这与神经科医生在实际临床实践中的评分方式相似。在这项研究中，85例帕金森病（PD）患者的震颤信号进行了测量，使用手表型的可穿戴设备组成的加速度计和陀螺仪。从测量的加速度和角速度计算位移和角度信号，并将加速度、角速度、位移和角度信号用于分析。从每个信号中提取19个特征，并使用成对相关策略来减少特征维数。选择的功能，决策树（DT），支持向量机（SVM），判别分析（DA），随机森林（RF），和k-最近邻（kNN）算法进行了探索自动评分的帕金森震颤的严重程度。所采用的分类器的性能进行了分析，使用准确率，召回率和精度，并在类似的研究中的其他结果进行比较。最后，对本文的局限性和进一步研究的计划进行了讨论。
Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.
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