Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 95, Issue -, Pages 140-146Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2018.02.007
Keywords
Parkinson's disease; Tremor; Wearable sensor; Machine learning; Convolutional neural network
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Funding
- Basic Science Research Program through the National Research Foundation of Korea(NRF) - Ministry of Science and ICT [NRF-2017R1A5A1015596]
- Institute of Medical and Biological Engineering, Medical Research Center (Seoul National University, Seoul, Korea)
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Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module. Neurologists assessed the tremor symptoms on the Unified Parkinson's Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured data were transformed into the frequency domain and used to construct a two-dimensional image for training the network, and the CNN model was trained by convolving tremor signal images with kernels. The proposed CNN architecture was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 0.85, linear weighted kappa = 0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method.
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