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Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease

文献类型：期刊论文

作者Cao, Jun6; Zhao, Yifan6; Shan, Xiaocai5,6; Blackburn, Daniel4; Wei, Jize3; Erkoyuncu, John Ahmet6; Chen, Liangyu2; Sarrigiannis, Ptolemaios G.1
刊名JOURNAL OF NEURAL ENGINEERING
出版日期2022-08-01
卷号19期号:4页码:19
ISSN号1741-2560
关键词electroencephalogram (EEG) revised Hilbert-Huang transformation (RHHT) peak frequency of cross-spectrum (PFoCS) support vector machine (SVM) topographic visualisation
DOI10.1088/1741-2552/ac84ac
英文摘要Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert-Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.
WOS关键词EMPIRICAL MODE DECOMPOSITION ; MILD COGNITIVE IMPAIRMENT ; HILBERT-HUANG TRANSFORM ; POWER SPECTRAL DENSITY ; ALPHA PEAK FREQUENCY ; SYNCHRONIZATION LIKELIHOOD ; EYES-OPEN ; DIAGNOSIS ; COHERENCE ; DISCRIMINATION
WOS研究方向Engineering ; Neurosciences & Neurology
语种英语
出版者IOP Publishing Ltd
WOS记录号WOS:000839502800001
源URL[http://ir.iggcas.ac.cn/handle/132A11/108419]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
通讯作者Zhao, Yifan
作者单位1.Royal Devon & Exeter NHS Fdn Trust, Exeter EX2 5DW, Devon, England
2.China Med Univ, Dept Neurosurg, Shengjing Hosp, Shenyang, Peoples R China
3.Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Peoples R China
4.NHS Fdn Trust, Royal Hallamshire Hosp, Sheffield Teaching Hosp, Dept Neurosci, Sheffield, S Yorkshire, England
5.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
6.Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield, Beds, England
推荐引用方式
GB/T 7714		 Cao, Jun,Zhao, Yifan,Shan, Xiaocai,et al. Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease[J]. JOURNAL OF NEURAL ENGINEERING,2022,19(4):19.
APA Cao, Jun.,Zhao, Yifan.,Shan, Xiaocai.,Blackburn, Daniel.,Wei, Jize.,...&Sarrigiannis, Ptolemaios G..(2022).Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease.JOURNAL OF NEURAL ENGINEERING,19(4),19.
MLA Cao, Jun,et al."Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease".JOURNAL OF NEURAL ENGINEERING 19.4(2022):19.

入库方式： OAI收割

来源：地质与地球物理研究所

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