Table_5_Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study.DOCX

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson’s Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.

本研究旨在对比不同时间窗口长度和特征数量下，针对健康受试者和帕金森病（PD）患者数据集的机器学习算法分类精度。参与研究的受试者包括32名健康受试者和18名帕金森病患者。研究通过加速度计和陀螺仪在受试者手部休息状态下获取惯性记录，并从中提取时间和时频域特征，以供七种机器学习算法使用：k近邻（kNN）、逻辑回归、支持向量机分类器（SVC）、线性判别分析、随机森林、决策树和高斯朴素贝叶斯。通过不同数量的提取特征（即272、190、136、82和27）以及不同时间窗口长度（即1、5、10和15秒）对分类器的精度进行比较。惯性记录表现为振荡波形，尤其在帕金森病患者中，其峰值出现在3至8赫兹的频率范围内。研究结果表明，最重要的特征包括平均频率、线性预测系数、功率比、功率密度偏斜和峰度。观察到测试阶段的精度计算高于训练阶段。在比较测试精度时，发现时间窗口长度与分类器类型之间存在显著交互作用（p < 0.05）。根据算法类型、时间窗口长度及其交互作用，研究发现了对估计精度具有显著影响的效应。kNN算法展现出最高的精度，而SVC算法表现最差。由1秒和5秒时间窗口提取的特征所驱动的kNN算法组合，具有较高的精度出现频率。使用少量特征进行分类导致算法决策相似。此外，性能随着使用特征数量的增加而显著提升，并在约136个特征处达到平台期。最后，本研究结果表明，kNN算法是分类帕金森病患者手部静止震颤的最佳算法。