Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device

作     者：Jeon, Hyoseon Lee, Woongwoo Park, Hyeyoung Lee, Hong Ji Kim, Sang Kyong Kim, Han Byul Jeon, Beomseok Park, Kwang Suk 

作者机构：Seoul Natl Univ Interdisciplinary Program Bioengn Seoul 03080 South Korea Seoul Natl Univ Hosp Dept Neurol Seoul 03080 South Korea Seoul Natl Univ Hosp Movement Disorder Ctr Seoul 03080 South Korea Seoul Natl Univ Coll Med Dept Biomed Engn Seoul 03080 South Korea 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2017年第17卷第9期

页      面：2067-2067页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Coway Co.  Ltd 

主　　题：tremor UPDRS automatic scoring Parkinson's disease wearable device machine learning algorithm 

摘      要：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.