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| Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease | |
| Cao, Jun1; Zhao, Yifan1; Shan, Xiaocai1,2; Blackburn, Daniel3; Wei, Jize4; Erkoyuncu, John Ahmet1; Chen, Liangyu5; Sarrigiannis, Ptolemaios G.6 | |
| 2022-08-01 | |
| Source Publication | JOURNAL OF NEURAL ENGINEERING
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| ISSN | 1741-2560 |
| Volume | 19Issue:4Pages:19 |
| Abstract | 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. |
| Keyword | electroencephalogram (EEG) revised Hilbert-Huang transformation (RHHT) peak frequency of cross-spectrum (PFoCS) support vector machine (SVM) topographic visualisation |
| DOI | 10.1088/1741-2552/ac84ac |
| WOS Keyword | EMPIRICAL MODE DECOMPOSITION ; MILD COGNITIVE IMPAIRMENT ; HILBERT-HUANG TRANSFORM ; POWER SPECTRAL DENSITY ; ALPHA PEAK FREQUENCY ; SYNCHRONIZATION LIKELIHOOD ; EYES-OPEN ; DIAGNOSIS ; COHERENCE ; DISCRIMINATION |
| Language | 英语 |
| WOS Research Area | Engineering ; Neurosciences & Neurology |
| WOS Subject | Engineering, Biomedical ; Neurosciences |
| WOS ID | WOS:000839502800001 |
| Publisher | IOP Publishing Ltd |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.iggcas.ac.cn/handle/132A11/108419 |
| Collection | 岩石圈演化国家重点实验室 |
| Corresponding Author | Zhao, Yifan |
| Affiliation | 1.Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield, Beds, England 2.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 3.NHS Fdn Trust, Royal Hallamshire Hosp, Sheffield Teaching Hosp, Dept Neurosci, Sheffield, S Yorkshire, England 4.Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Peoples R China 5.China Med Univ, Dept Neurosurg, Shengjing Hosp, Shenyang, Peoples R China 6.Royal Devon & Exeter NHS Fdn Trust, Exeter EX2 5DW, Devon, England |
| Recommended Citation 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. |
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