Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920620 | Computers in Biology and Medicine | 2018 | 15 Pages |
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 (accuracyâ¯=â¯0.85, linear weighted kappaâ¯=â¯0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Han Byul Kim, Woong Woo Lee, Aryun Kim, Hong Ji Lee, Hye Young Park, Hyo Seon Jeon, Sang Kyong Kim, Beomseok. Jeon, Kwang S. Park,