基于多尺度排序递归图的糖尿病轻度认知障碍脑电信号分析

doi:10.3969/j.issn.0258-8021.2020.03.08

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Abstract Studying the EEG characteristics in mild cognitive impairment (MCI) of diabetes is helpful to the early prevention and diagnosis of the disease. Based on the multi-scale recurrence plot. We analyzed the deterministic characteristics of 15-electrode eye-closed resting state EEG of 14 patients with mild diabetes mellitus MCI(DM-MCI) and 11 patients with normal cognitive function of diabetes mellitus(DM-nMCI), and 16 patients with mild cognitive impairment without diabetes mellitus(nDM-MCI) and 10 patients with normal cognitive function without diabetes mellitus (nDM-nMCI). Deterministic values between the four groups were statistically analyzed using one-way ANOVA, and the correlations between deterministic values and cognitive function of each electrode were calculated by Pearson correlation method. The results showed that at small scale1≤s≤4, the values of DET in patients with nDM-MCI and nDM-nMCI were significantly higher than those with DM-MCI and DM-nMCI; At large scale12≤s≤15, four groups of DET values tend to be consistent. The deterministic values of all electrodes at different scales in the DM-MCI group were higher DM-nMCI group. The deterministic values of MCI at C3 (DM-MCI: 0.83±0.09, nDM-MCI: 0.86±0.09) and C4(DM-MCI: 0.85±0.08, nDM-MCI: 0.88±0.08) electrodes increased significantly at different scales, which were important characteristics in distinguishing MCI and their correlations between DET and neuropsychological test scores Boston (P=0.033), FAQ (P=0.026), WAIS (P=0.037), and MoCA (P=0.039, P=0.017) was significant. The DET values of Fp1 (DM-MCI: 0.80±0.09, DM-nMCI: 0.75±0.07), Fp2(DM-MCI: 0.80±0.09, DM-nMCI: 0.75±0.01) and Oz (DM-MCI: 0.76±0.06, DM-nMCI: 0.73±0.07) at small scale, and Fp2 (DM-MCI: 0.96±0.03, DM-nMCI: 0.94±0.01) at big scale increased significantly, which are the important characteristics in distinguishing DM. It was suggested that the determinacy based on multi-scale order recurrence plot was the EEG feature associated with cognitive decline and DM.

Key words： mild cognitive impairment (MCI) multi-scale determination order recurrence plot

Received: 12 July 2018

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R318

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	Articles by authors
	Gu Guanghua
	Lou Chunyang
	Cui Dong
	Hou Junbo
	Li Zhaohui
	Li Xiaoli
	Yin Shimin
	Wang Lei

Cite this article:

Gu Guanghua,Lou Chunyang,Cui Dong, et al. Multi-Scale Recurrence Plot Based EEG Signal Analysis of Diabetes Mellitus and Mild Cognitive Impairment[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 311-317.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.03.08 OR http://cjbme.csbme.org/EN/Y2020/V39/I3/311

[1] Koekkoek PS, Rutten GEHM, Ruis C, et al. Mild depressive symptoms do not influence cognitive functioning in patients with type 2 diabetes[J]. Psychoneuroendocrinology, 2013, 38(3): 376-386.
[2] Lee SJ, Han JH, Hwang JW, et al. Screening for normal cognition, mild cognitive impairment and dementia with the korean dementia screening questionnaire[J]. Psychiatry Investig, 2018, 15(4):384-389.
[3] Huckans M, Hutson L, Twamley E, et al. Efficacy of cognitive rehabilitation therapies for mild cognitive impairment (MCI) in older adults: Working toward a theoretical model and evidence-based interventions[J]. Neuropsychology Review, 2013, 23(1): 63-80.
[4] Qiu C, Sigurdsson S, Zhang Q, et al. Diabetes, markers of brain pathology and cognitive function: The age, gene/environment susceptibility-reykjavik study[J]. Annals of Neurology, 2014, 75(1):138-146.
[5] Wild S, Roglie G, Geren A, et al. Global pervalence of dibaetes: estimates for the year 2000 and projections for 2030[J]. Dibaetes Care, 2004:27(5): 1047-1053.
[6] Eckmann JP, Kamphorst S, Ruelle D. Recurrence plots of dynamical Systems[J]. Europhysics Letters, 1987, 5: 973-977.
[7] Thomasson N, Hoeppner NT, Webber Jr CL, et al. Recurrence quantification in Epileptic EEGs[J]. Physics Letters A, 2001, 279(1-2): 94-101.
[8] Ouyang GX, Li XL, Dang CY, et al. Using recurrence plot for determinism analysis of EEG recording in genetic absence epilepsy rats[J]. Clinical Neurophysiology, 2008, 119(8): 1747-1755.
[9] Deng B, Cai L, Li S, et al. Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease[J]. Cognitive Neurodynamics, 2017, 11(3):1-15.
[10] Jian YU, Cao J, Wei W, et al. Recurrence plot and recurrence quantification analysis of human gait complexity[J]. Journal of Xian Jiaotong University, 2017, 51(10):47-52, 70.
[11] 崔冬, 蒲伟婷, 李小俚, 等. 基于全局排序模式同步的多通道脑电同步特性分析[J]. 中国生物医学工程学报, 2017, 36(2):136-142.
[12] Cui D, Pu W T, Liu J, et al. A new EEG synchronization strength analysis method: S-estimator based normalized weighted- permutation mutual information[J]. Neural Networks. 2016, 82: 30-38.
[13] Bono V, Das S, Jamal W, et al. Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG[J]. J Neurosci Methods, 2018, 267:89-107.
[14] Stam CJ, Jelles B, Achtereekte HAM, et al. Investigation of EEG non-linearity in dementia and Parkinson's disease[J]. Electroencephalography and Clinical Neurophysiology, 1995, 95(5): 309-317.
[15] Jeong J, Kim SY and Han SH. Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension [J]. Electroencephalogr Clin Neurophysiol, 1998, 106(3): 220-228.
[16] Abásolo D, Hornero R, Gómez C, et al. Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure[J]. Med Eng Phys, 2006, 28(4): 315-322.
[17] Roberts RO, Knopman DS, Geda YE, et al. Association of diabetes with amnestic and nonamnestic mild cognitive impairment[J]. Alzheimers Dement, 2014, 10(1): 18-26.
[18] Labate D, La Foresta F, Morabito G, et al. Entropic measures of EEG complexity in Alzheimer's disease through a multivariate multiscale approach. IEEE Sens J, 2013, 13(9): 3284-3292.
[19] Tylová L, Kukal J, Hubata-Vacek V, et al. Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classifification [J]. Biomed Signal Process Control, 2018, 39:424-430.
[20] Tzimourta KD, Afrantou T, Ioannidis P, et al. Analysis of electroencephalographic signals complexity regarding Alzheimer's disease [J]. Computers and Electrical Engineering, 2019, 76: 198-212
[21] Coronel C, Garn H, Waser M, et al. Quantitative EEG markers of entropy and auto mutual information in relation to MMSE scores of probable Alzheimer's disease patients[J]. Entropy, 2017, 19(3):1-14.
[22] Cui D, Wang J H, Wang L, et al. Symbol recurrence plots based resting-state eyes-closed EEG deterministic analysis on amnestic mild cognitive impairment in type 2 diabetes mellitus[J]. Neurocomputing, 2016, 203: 102-110.
[23] Timothy LT, Krishna BM and Nair U. Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis[J]. International Journal of Psychophysiology, 2017, 120: 86-95.
[24] Park JH, Kim S, Kim CH, et al. Multiscale entropy of EEG from patients under different pathological conditions[J]. Fractals, 2007, 15(4): 399-404.
[25] Yang AC, Wang SJ, Lai KL et al. Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease[J]. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 2013, 47: 52-61.
[26] Mizuno T, Takahashi T, Cho RY, et al. Assessment of EEG dynamical complexity in Alzheimer's disease using multiscale entropy[J]. Clinical Neurophysiology, 2010, 121: 1438-1446.