Knowledge Commons of Institute of Automation,CAS
Group feature learning and domain adversarial neural network for aMCI diagnosis system based on EEG | |
Fan, Chen-Chen![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() | |
2021 | |
会议名称 | International Conference on Robotics and Automation |
会议日期 | 2021.06 |
会议地点 | 西安 |
出版者 | IEEE |
摘要 | Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system. |
语种 | 英语 |
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/173211/51861 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, China. 4.CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Institute of Systems Engineering, Macau University of Science and Technology, China. 5.Department of Neurology, First People's Hospital of Foshan, Foshan, China |
推荐引用方式 GB/T 7714 | Fan, Chen-Chen,Xie, Haiqun,Peng, Liang,et al. Group feature learning and domain adversarial neural network for aMCI diagnosis system based on EEG[C]:IEEE,2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Group feature learni(1846KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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。
修改评论