A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties
文献类型:期刊论文
作者 | Chen Sheng2,3![]() ![]() ![]() ![]() ![]() |
刊名 | IEEE Transactions on Neural Systems and Rehabilitation Engineering
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出版日期 | 2024 |
卷号 | 32页码:1456-1465 |
关键词 | Dementia multi-modal machine learning domain-adversarial neural network |
产权排序 | 1 |
英文摘要 | Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia. |
URL标识 | 查看原文 |
语种 | 英语 |
WOS记录号 | WOS:001197793100003 |
源URL | [http://ir.ia.ac.cn/handle/173211/56689] ![]() |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Yang Hongjun; Hou Zeng-Guang |
作者单位 | 1.First People's Hospital of Foshan, Foshan 528000, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China 4.Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Peoples R China 5.Rehabilitation Hospital Affiliated to National Research Center for Rehabilitation Technical Aids, Beijing 100176, Peoples R China |
推荐引用方式 GB/T 7714 | Chen Sheng,Zhang Chutian,Yang Hongjun,et al. A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2024,32:1456-1465. |
APA | Chen Sheng.,Zhang Chutian.,Yang Hongjun.,Peng Liang.,Xie Haiqun.,...&Hou Zeng-Guang.(2024).A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.IEEE Transactions on Neural Systems and Rehabilitation Engineering,32,1456-1465. |
MLA | Chen Sheng,et al."A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties".IEEE Transactions on Neural Systems and Rehabilitation Engineering 32(2024):1456-1465. |
入库方式: OAI收割
来源:自动化研究所
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