Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network
| Autor: | Sang Kyong Kim, Woong-Woo Lee, Kwang Suk Park, Hye Young Park, Hong Ji Lee, Han Byul Kim, Hyo Seon Jeon, Aryun Kim, Beomseok Jeon |
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| Rok vydání: | 2017 |
| Předmět: |
Male
Parkinson's disease Computer science Health Informatics 02 engineering and technology Wrist Accelerometer Convolutional neural network 03 medical and health sciences Wearable Electronic Devices 0302 clinical medicine Rating scale Accelerometry Tremor 0202 electrical engineering electronic engineering information engineering medicine Humans In patient Aged Monitoring Physiologic business.industry Pattern recognition Parkinson Disease Middle Aged medicine.disease nervous system diseases Computer Science Applications medicine.anatomical_structure 020201 artificial intelligence & image processing Female Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery |
| Zdroj: | Computers in biology and medicine. 95 |
| ISSN: | 1879-0534 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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