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A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors | |
Wang, Chen1![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
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ISSN | 2379-8920 |
2022-12-01 | |
卷号 | 14期号:4页码:1553-1563 |
摘要 | Parkinson's disease (PD) is the second most common neurodegenerative disorder and the heterogeneity of early PD leads to interrater and intrarater variability in observation based clinical assessment. Thus, objective monitoring of PD induced motor abnormalities has attracted significant attention to manage disease progression. Here, we proposed a hierarchical architecture to reliably detect abnormal characteristics and comprehensively quantify the multisymptom severity in patients with PD. A novel wearable device was designed to measure motor features in 15 PD patients and 15 age-matched healthy subjects, while performing five types of motor tasks. The abnormality classes of multimodal measurements were recognized by hidden Markov models (HMMs) in the first layer of the proposed architecture, aiming at motivating the evaluation of specific motor manifestations. Subsequently, in the second layer, three single symptom models differentiated PD motor characteristics from normal motion patterns and quantified the severity of cardinal PD symptoms in parallel. In order to further analyze the disease status, the multilevel severity quantification was fused in the third layer, where machine learning algorithms were adopted to develop a multisymptom severity score. The experimental results demonstrated that the quantification of three cardinal symptoms was highly accurate to distinguish PD patients from healthy controls. Furthermore, strong correlations were observed between the Unified PD Rating Scale (UPDRS) scores and the predicted subscores for tremor (R = 0.75, P = 1.40e - 3), bradykinesia (R = 0.71, P = 2.80e - 3), and coordination impairments (R = 0.69, P = 4.20e - 3), and the correlation coefficient can be enhanced to 0.88 (P = 1.26e - 5) based on the fusion schemes. In conclusion, the proposed assessment architecture holds great promise to push forward the in-home monitoring of clinical manifestations, thus enabling the self-assessment of disease progression. |
关键词 | Diseases Machine learning Hidden Markov models Accelerometers Monitoring Gyroscopes Parkinson's disease Wearable computing Sensor systems multilevel fusion multisymptom assessment Parkinson's disease (PD) wearable sensor system |
DOI | 10.1109/TCDS.2021.3123157 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[61720106012] ; Beijing Natural Science Foundation[Z170003] ; Chinese Academy of Science[XDB32040000] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Chinese Academy of Science |
WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Robotics ; Neurosciences |
WOS记录号 | WOS:000916821100021 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+医疗 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51366 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Hou, Zeng-Guang |
作者单位 | 1.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Med Sci, Peking Union Med Coll Hosp, Peking Union Med Coll, Beijing 100730, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Chen,Peng, Liang,Hou, Zeng-Guang,et al. A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2022,14(4):1553-1563. |
APA | Wang, Chen,Peng, Liang,Hou, Zeng-Guang,Li, Yanfeng,Tan, Ying,&Hao, Honglin.(2022).A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,14(4),1553-1563. |
MLA | Wang, Chen,et al."A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 14.4(2022):1553-1563. |
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