Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network
Abstract 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. Highlights We propose tremor score prediction algorithm for Parkinson’s disease patients, based on Convolutional Neural Network (CNN). CNN was firstly used to estimate clinical tremor score, using the signals collected from wearable device. The proposed deep learning architecture demonstrated an average accuracy of 85% and a linear weighted kappa of 0.85. CNN showed the superior accuracy compared to the machine learning methods trained by features in previous studies.
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- DOI : http://dx.doi.org/10.1016/j.compbiomed.2018.02.007
- Elsevier : 저널 > 논문
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