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Sensors (Basel). 2017 Sep 9;17(9). pii: E2067. doi: 10.3390/s17092067.

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

Author information

1
The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea. nulpurunhs@bmsil.snu.ac.kr.
2
Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea. w2pooh@daum.net.
3
Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea. 0907bluelove@naver.com.
4
The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea. hongjidan@bmsil.snu.ac.kr.
5
The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea. skkim@bmsil.snu.ac.kr.
6
The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea. hahanbyul@bmsil.snu.ac.kr.
7
Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea. brain@snu.ac.kr.
8
Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Korea. pks@bmsil.snu.ac.kr.

Abstract

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.

KEYWORDS:

Parkinson’s disease; UPDRS; automatic scoring; machine learning algorithm; tremor; wearable device

PMID:
28891942
PMCID:
PMC5621347
DOI:
10.3390/s17092067
[Indexed for MEDLINE]
Free PMC Article

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