中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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; Zhang Chutian4; Yang Hongjun2,3; Peng Liang2,3; Xie Haiqun1; Lv Zeping5; Hou Zeng-Guang2,3
刊名IEEE Transactions on Neural Systems and Rehabilitation Engineering
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

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。