The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea.
Physiol Meas. 2017 Oct 31;38(11):1980-1999. doi: 10.1088/1361-6579/aa8e1f.
Although clinical aspirations for new technology to accurately measure and diagnose Parkinsonian tremors exist, automatic scoring of tremor severity using machine learning approaches has not yet been employed.
This study aims to maximize the scientific validity of automatic tremor-severity classification using machine learning algorithms to score Parkinsonian tremor severity in the same manner as the unified Parkinson's disease rating scale (UPDRS) used to rate scores in real clinical practice.
Eighty-five PD patients perform four tasks for severity assessment of their resting, resting with mental stress, postural, and intention tremors. The tremor signals are measured using a wristwatch-type wearable device with an accelerometer and gyroscope. Displacement and angle signals are obtained by integrating the acceleration and angular-velocity signals. Nineteen features are extracted from each of the four tremor signals. The optimal feature configuration is decided using the wrapper feature selection algorithm or principal component analysis, and decision tree, support vector machine, discriminant analysis, and k-nearest neighbour algorithms are considered to develop an automatic scoring system for UPDRS prediction. The results are compared to UPDRS ratings assigned by two neurologists.
The highest accuracies are 92.3%, 86.2%, 92.1%, and 89.2% for resting, resting with mental stress, postural, and intention tremors, respectively. The weighted Cohen's kappa values are 0.745, 0.635 and 0.633 for resting, resting with mental stress, and postural tremors (almost perfect agreement), and 0.570 for intention tremors (moderate).
These results indicate the feasibility of the proposed system as a clinical decision tool for Parkinsonian tremor-severity automatic scoring.
尽管临床对新技术的期望是准确地测量和诊断帕金森震颤，但使用机器学习方法自动对震颤严重程度进行评分尚未得到应用。
本研究旨在使用机器学习算法最大限度地提高自动震颤严重程度分类的科学有效性，以便以与用于在实际临床实践中评分的统一帕金森病评定量表 (UPDRS) 相同的方式对帕金森震颤严重程度进行评分。
85 名 PD 患者进行四项任务，以评估其静止、静止伴精神压力、姿势和意向震颤的严重程度。使用带有加速度计和陀螺仪的手表式可穿戴设备测量震颤信号。通过对加速度和角速度信号进行积分来获得位移和角度信号。从四个震颤信号中的每一个提取 19 个特征。使用包装特征选择算法或主成分分析来决定最佳特征配置，并考虑决策树、支持向量机、判别分析和 k-最近邻算法来开发 UPDRS 预测的自动评分系统。将结果与两名神经科医生分配的 UPDRS 评分进行比较。
静止、静止伴精神压力、姿势和意向震颤的最高准确率分别为 92.3%、86.2%、92.1%和 89.2%。静止、静止伴精神压力和姿势震颤的加权 Cohen's kappa 值分别为 0.745、0.635 和 0.633（几乎完美一致），意向震颤为 0.570（中度）。
这些结果表明，所提出的系统作为帕金森震颤严重程度自动评分的临床决策工具具有可行性。
