CASIA OpenIR  > 多模态人工智能系统全国重点实验室
A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties
Chen Sheng1,2; Zhang Chutian3; Yang Hongjun1,2; Peng Liang1,2; Xie Haiqun4; Lv Zeping5; Hou Zeng-Guang1,2
Source PublicationIEEE Transactions on Neural Systems and Rehabilitation Engineering
ISSN1534-4320
2024
Volume32Pages:1456-1465
Corresponding AuthorYang, Hongjun(hongjun.yang@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
Contribution Rank1
Abstract

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. 

KeywordDementia multi-modal machine learning domain-adversarial neural network
DOI10.1109/TNSRE.2024.3379891
WOS KeywordSENILE-DEMENTIA ; EEG
URL查看原文
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaEngineering ; Rehabilitation
WOS SubjectEngineering, Biomedical ; Rehabilitation
WOS IDWOS:001197793100003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IS Representative Paper
Sub direction classification人工智能+医疗
planning direction of the national heavy laboratory其他
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56689
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorYang Hongjun; Hou Zeng-Guang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
3.Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Peoples R China
4.First People's Hospital of Foshan, Foshan 528000, Peoples R China
5.Rehabilitation Hospital Affiliated to National Research Center for Rehabilitation Technical Aids, Beijing 100176, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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