Knowledge Commons of Institute of Automation,CAS
A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments | |
Chen Sheng1; Xie Haiqun3; Yang Hongjun1; Fan Chenchen1; Hou Zeng-Guang1; Zhang Chutian1,2 | |
2022 | |
会议名称 | China Intelligent Robotics Annual Conference |
页码 | 349–361 |
会议日期 | 2022.12.16-2022.12.18 |
会议地点 | Xi'an |
出版者 | Springer |
摘要 | Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer's disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the impact of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework. |
关键词 | Mild cognitive impairment EEG Machine learning |
是否为代表性论文 | 是 |
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 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56688 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Hou Zeng-Guang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Macau Univ Sci & Technol, CASIA MUST Joint Lab Intelligence Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.First People's Hospital of Foshan, Foshan 528000, 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 | Chen Sheng,Xie Haiqun,Yang Hongjun,et al. A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments[C]:Springer,2022:349–361. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Classification Fra(974KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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