Affiliations: [a] Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China | [b] Department of Neurosurgery, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China | [c] Shenzhen University School of Medicine, Shenzhen, Guangdong, China | [d] Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China
Correspondence:
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Corresponding author: %Cuiwei␣Yang,␣Center␣for␣Biomedical␣Engineering,␣School␣of␣Information␣Science␣and␣Technology,␣Fudan␣University,␣Shanghai␣200433,␣China.␣Tel.:␣+86␣21␣31242520;␣E-mail:␣[email protected]. Xiaodong Cai, Department of Neurosurgery, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, China. Tel.: +86 755 86392066; E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.
