An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment

doi:10.1016/j.bspc.2024.106646

	An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment
	Cheng, Shiyu 1,3; Shang, Pan 4; Zhang, Yingwei 1,2; Guan, Jianhe 3; Chen, Yiqiang 1,2; Lv, Zeping 5; Huang, Shuyun 4; Liu, Yajing 4; Xie, Haiqun 4
	2024-10-01
发表期刊	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
卷号	96 页码:12
摘要	Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment and early diagnosis of aMCI are crucial in preventing the continued deterioration of cognitive abilities; nevertheless, this task poses a formidable challenge due to the inconspicuous nature of early symptoms. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost, and user-friendly neuroimaging technique, which is capable of detecting subtle changes in brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from different perspectives and enhance auxiliary diagnosis accuracy significantly. This paper proposes an fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of aMCI. Specifically, we convert one-dimensional time-series fNIRS signals into two-dimensional images with Gramian Angular Field and achieve end-to-end fNIRS representation with convolutional neural network. Then, we integrate the extracted features with cognitive scales at the decision-making level to improve the diagnosis accuracy of aMCI, employing the data balance strategy to prevent biased prediction. What is more, based on the fNIRS features, we also propose a data-driven cognitive scales-screening method to help the physician to assess aMCI with higher efficiency. We conducted experiments on 86 subjects (including 53 aMCI patients and 33 normal controls) recruited from Foshan First People's Hospital. The diagnosis accuracy reaches 88.02% and 93.90% with fNIRS representation and further fNIRSscales fusion, respectively. With the cognitive scales-screening, we delete 50% scales, reducing test time but only losing 2.54% accuracy.
关键词	Mild cognitive impairment Auxiliary diagnosis fNIRS Scales Multimodal fusion Scales screening
DOI	10.1016/j.bspc.2024.106646
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[62302487] ; Beijing Municipal Science & Technology Commission[Z221100002722009] ; Science and Technology Innovation Program of Hunan Province[2024JJ9031] ; Science and Technology Innovation Program of Hunan Province[2022RC4006]
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical
WOS记录号	WOS:001275091700001
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39674
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yingwei; Xie, Haiqun
作者单位	1.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.China Univ Geosci, Beijing 100083, Peoples R China 4.First Peoples Hosp Foshan, Dept Neurol, Foshan 528010, Guangdong, Peoples R China 5.Natl Res Ctr Rehabil Tech Aids, Beijing Key Lab Rehabil Tech Aids Old Age Disabil, Beijing 100176, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Shiyu,Shang, Pan,Zhang, Yingwei,et al. An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2024,96:12.
APA	Cheng, Shiyu.,Shang, Pan.,Zhang, Yingwei.,Guan, Jianhe.,Chen, Yiqiang.,...&Xie, Haiqun.(2024).An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,96,12.
MLA	Cheng, Shiyu,et al."An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 96(2024):12.