Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson's Disease Based on Wearable Sensors
文献类型：期刊论文
| 作者 | Li, Bochen1,2; Yao, Zhiming1 ; Wang, Jianguo1,2; Wang, Shaonan1,2; Yang, Xianjun1; Sun, Yining1
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| 刊名 | ELECTRONICS
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| 出版日期 | 2020-11-01 |
| 卷号 | 9 |
| 关键词 | Parkinson's disease freezing of gait detection deep learning convolutional neural networks long short-term memory attention mechanism squeeze-and-excitation block data augmentation |
| DOI | 10.3390/electronics9111919 |
| 通讯作者 | Yao, Zhiming(zhmyao@iim.ac.cn) |
| 英文摘要 | Freezing of gait (FOG) is a paroxysmal dyskinesia, which is common in patients with advanced Parkinson's disease (PD). It is an important cause of falls in PD patients and is associated with serious disability. In this study, we implemented a novel FOG detection system using deep learning technology. The system takes multi-channel acceleration signals as input, uses one-dimensional deep convolutional neural network to automatically learn feature representations, and uses recurrent neural network to model the temporal dependencies between feature activations. In order to improve the detection performance, we introduced squeeze-and-excitation blocks and attention mechanism into the system, and used data augmentation to eliminate the impact of imbalanced datasets on model training. Experimental results show that, compared with the previous best results, the sensitivity and specificity obtained in 10-fold cross-validation evaluation were increased by 0.017 and 0.045, respectively, and the equal error rate obtained in leave-one-subject-out cross-validation evaluation was decreased by 1.9%. The time for detection of a 256 data segment is only 0.52 ms. These results indicate that the proposed system has high operating efficiency and excellent detection performance, and is expected to be applied to FOG detection to improve the automation of Parkinson's disease diagnosis and treatment. |
| WOS关键词 | NETWORKS ; LEVODOPA ; EPISODES |
| 资助项目 | National Natural Science Foundation of China (NSFC) for Youth[61701483] ; National Key Research and Development Program of China[2016YFB1001300] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000593599400001 |
| 资助机构 | National Natural Science Foundation of China (NSFC) for Youth ; National Key Research and Development Program of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/105499]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Yao, Zhiming |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Bochen,Yao, Zhiming,Wang, Jianguo,et al. Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson's Disease Based on Wearable Sensors[J]. ELECTRONICS,2020,9. |
| APA | Li, Bochen,Yao, Zhiming,Wang, Jianguo,Wang, Shaonan,Yang, Xianjun,&Sun, Yining.(2020).Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson's Disease Based on Wearable Sensors.ELECTRONICS,9. |
| MLA | Li, Bochen,et al."Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson's Disease Based on Wearable Sensors".ELECTRONICS 9(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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