Advances in the Extraction and Classification of EEG Dynamic Features in Patients with Mild Cognitive Impairment
Li Hengzhi1,2, Wen Dong2*, Wei Zhenhao2, Zhou Yanhong2,3
1(Department of Primary Education, Hebei Institute of International Business and Economics, Qinhuangdao 06631, Hebei, China); 2(College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China); 3(School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, Hebei, China)
Abstract：Electroencephalography (EEG) can be used to assess pathological changes in mild cognitive impairment (MCI) disorders. In recent years, the feature extraction and classification methods in the field of EEG have been widely applied to the diagnosis of mild cognitive impairment diseases. In order to understand the current development of MCI EEG signal analysis, this work first analyzed in depth the application of EEG signal from MCI patients in the field of feature extraction, its advantages and disadvantages from two aspects of local coupling and global synchronization. Then the method of classification of EEG signal from MCI patients was summarized and analyzed, such as support vector machines, K-means, and convolutional neural networks that have been widely used in recent years. Finally, the future development trend of dynamic feature extraction and classification methods was prospected
李恒智, 文冬, 魏振豪, 周艳红. 轻度认知障碍患者EEG动力学特征提取与分类方法研究进展[J]. 中国生物医学工程学报, 2019, 38(3): 348-354.
Li Hengzhi, Wen Dong, Wei Zhenhao, Zhou Yanhong. Advances in the Extraction and Classification of EEG Dynamic Features in Patients with Mild Cognitive Impairment. Chinese Journal of Biomedical Engineering, 2019, 38(3): 348-354.
[1] Pikovskij AS, Rosenblum MG. Detecting direction of coupling in interacting oscillators [J]. Phys Rev E, 2001, 64: 045202. [2] Michels L, Muthuraman M, Anwar AR, et al. Changes of functional and directed resting-state connectivity are associated with neuronal oscillations, ApoE genotype and amyloid eposition in mild cognitive impairment [J]. Front Aging Neurosci, 2017, 9: 304. [3] Brassen S, Braus DF, Weber-Fahr W, et al. Late-onset depression with mild cognitive deficits: electrophysiological evidences for a preclinical dementia syndrome [J]. Dement Geriatr Cogn Disord, 2004, 18(3-4): 271-277. [4] Musaeus CS, Engedal K, Hogh P, et al. EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer′s Disease [J]. J Alzheimers Dis, 2018, 64(4): 1359-1371. [5] McBride J, Zhao Xiaopeng, Munro N, et al. Discrimination of mild cognitive impairment and Alzheimer′s Disease using transfer entropy measures of scalp EEG [J]. J Healthc Eng, 2015, 6(1): 55-70. [6] Houmani N, Vialatte F, Gallego-Jutgla E, et al. Diagnosis of Alzheimer′s disease with electroencephalography in a differential framework [J]. PLoS ONE, 2018, 13(3): e0193607. [7] Liu Chia-Ju, Huang Chin-Fei, Chou Chia-Yi, et al. Age- and disease-related features of task-related brain oscillations by using mutual information [J]. Brain Behav, 2012, 2(6): 754-762. [8] Ruiz-Gomez SJ, Gomez C, Poza J, et al. Automated multiclass classification of spontaneous EEG activity in Alzheimer′s disease and mild cognitive impairment [J]. Entropy, 2018, 20(1):35. [9] Cui Dong, Wang Jinhuan, Bian Zhijie, et al. Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus [J]. J Innov Opt Heal Sci, 2015, 8(5): 1550010. [10] Babiloni C, Ferri R, Binetti G, et al. Fronto-parietal coupling of brain rhythms in mild cognitive impairment: a multicentric EEG study [J]. Brain Res Bull, 2006, 69(1): 63-73. [11] Stam CJ, Van DMY, Pijnenburg YA, et al. EEG synchronization in mild cognitive impairment and Alzheimer′s disease [J]. Acta Neurol Scand, 2003, 108(2): 90-96. [12] Ahmadlou M, Adeli H. Fuzzy synchronization likelihood with application to attention-deficit / hyperactivity disorder [J]. Clin EEG Neurosci, 2011, 42(1): 6-13. [13] Al-Qazzaz NK, Ali S, Ahmad SA, et al. Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition [C] //The 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society proceedings. Seogwipo: IEEE, 2017: 3174-3177. [14] Fiscon G, Weitschek E, Cialini A, et al. Combining EEG signal processing with supervised methods for Alzheimer′s patients classification [J]. BMC Med Infom Decis, 2018, 18: 35. [15] Al-Qazzaz NK, Ali S, Ahmad SA, et al. Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis [J]. Med Biol Eng Comput, 2018, 56(1): 137-157. [16] Rosenblum MG, Cimponeriu L, Bezerianos A, et al. Identification of coupling direction: application to cardiorespiratory interaction [J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2002, 65(4 Pt 1): 041909. [17] Wen Dong, Bian Zhijie, Li Qiuli, et al. Resting-state EEG coupling analysis of amnestic mild cognitive impairment with T2DM by using permutation conditional mutual information [J]. Clin Neurophysiol, 2016, 127(1): 335-348. [18] Babiloni C, Frisoni GB, Pievani M, et al. White matter vascular lesions are related to parietal-to-frontal coupling of EEG rhythms in mild cognitive impairment [J]. Hum Brain Mapp, 2008, 29(12): 1355-1367. [19] Vecchio F, Babiloni C. Direction of information flow in Alzheimer′s Disease and MCI patients [J]. Int J Alzheimers Dis, 2011: 214580. [20] Li Xiaoli, Ouyang Gaoxiang. Estimating coupling direction between neuronal populations with permutation conditional mutual information [J]. Neuroimage, 2010, 52(2): 497-507. [21] Li Zhaohui, Ouyang Gaoxiang, Li Duan, et al. Characterization of the causality between spike trains with permutation conditional mutual information [J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2011, 84(2 Pt 1): 021929. [22] 杨骏,闫拓,薛青,等. 老年人轻度认知障碍部分定向相干脑网络分析 [J]. 中国生物医学工程学报, 2013, 32(4): 390-396. [23] McBride JC, Zhao Xiaopeng, Munro NB, et al. Sugihara causality analysis of scalp EEG for detection of early Alzheimer′s disease [J]. Neuroimage Clin, 2015, 7: 258-265. [24] Wen Dong, Xue Qing, Lu Chengbiao, et al. A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment [J]. Neural Networks, 2014, 56: 1-9. [25] Koenig T, Prichep L, Dierks T, et al. Decreased EEG synchronization in Alzheimer′s disease and mild cognitive impairment [J]. Neurobiol Aging, 2005, 26(2): 165-171. [26] Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer′s disease from EEG signals: where are we standing? [J]. Curr Alzheimer Res, 2010, 7(6): 487-505. [27] Allefeld C, Kurths J. An approach to multivariate phase synchronization analysis and its application to event-related potentials [J]. Int J Bifurcat Chaos, 2004, 14(2): 417-426. [28] Carmeli C, Knyazeva MG, Innocenti GM, et al. Assessment of EEG synchronization based on state-space analysis [J]. Neuroimage, 2005, 25(2): 339-354. [29] Toth B, File B and R Boha. EEG network connectivity changes in mild cognitive impairment - preliminary results [J]. Int J Psychophysiol, 2014, 92(1): 1-7. [30] Kramer MA, Chang Fenlei, Cohen ME, et al. Synchronization measures of the scalp electroencephalogram can discriminate healthy from Alzheimer′s subjects [J]. Int J Neural Syst, 2007, 17(2): 61-69. [31] Sweeney-Reed CM, Riddell PM, Ellis JA, et al. Neural correlates of true and false memory in mild cognitive impairment [J]. PLoS ONE, 2012, 7(10): e48357. [32] 赵建强,高云,魏玲,等. 认知障碍患者theta节律脑功能网络连接异常 [J]. 生物物理学报, 2014, 30(5): 369-379. [33] Dauwels J, Vialatte F, Musha T, et al. A comparative study of synchrony measures for the early diagnosis of Alzheimer′s disease based on EEG [J]. Neuroimage, 2010, 49(1): 668-693. [34] Koenig T, Lehmann D, Saito N, et al. Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naive patients with schizophrenia: preliminary results [J]. Schizophr Res, 2001, 50(1-2): 55-60. [35] Dauwels J, Vialatte F, Weber T, et al. Quantifying statistical interdependence by message passing on graphs-part I: one-dimensional point processes [J]. Neural Comput, 2009, 21(8): 2152-2202. [36] Dauwels J, Vialatte F, Weber T, et al. Quantifying statistical interdependence by message passing on graphs-part II: multidimensional point processes [J]. Neural Comput, 2009, 21(8): 2203-2268. [37] Cui Dong, Liu Xianzeng, Wan You, et al. Estimation of genuine and random synchronization in multivariate neural series [J]. Neural Networks, 2010, 23(6): 698-704. [38] Timothy LT, Krishna BM, Nair U. Cross recurrence quantification analysis of mild cognitive impairment EEG under working memory condition [C] // 2017 IEEE Life Sciences Conference (LSC). Sydney: IEEE, 2017: 145-148. [39] Esteve GJ, Jordi SC, Francois V, et al. A hybrid feature selection approach for the early diagnosis of Alzheimer′s disease [J]. J Nural Eng, 2015, 12(1): 1-16. [40] Taishi U, Toshimitsu M, Takashi A, et al. Classification method for mild cognitive impairment based on power variability of EEG using only a few electrodes [J]. Electr Commun Jpn, 2016, 99(11): 107-114. [41] Mcbride JC, Zhao Xiaopeng, Munro NB, et al. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer′s disease [J]. Comput Methods Programs Biomed, 2014, 114(2): 153-163. [42] Mcbride JC, Zhao Xiaopeng, Munro NB, et al. Resting EEG discrimination of early stage Alzheimer′s disease from normal aging using inter-channel coherence network graphs [J]. Ann Biomed Eng, 2013, 41(6): 1233-1242. [43] Li Xiaoou, Yan Yuning, Wei Wenshi. Identifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP [J]. Comput Math Method, 2013: 658501. [44] Baker MC, Kerr AS, Hames H, et al. An SFFS technique for EEG feature classification to identify sub-groups [C] //Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems. Rome: IEEE, 2012: 1-4. [45] Poza J, Gomez C, Garcia M, et al. Spatio-Temporal fluctuations of neural dynamics in mild cognitive impairment and Alzheimer′s Disease [J]. Curr Alzheimer Res, 2017, 14(9): 924-936. [46] Akrofi K, Baker MC, O′Boyle MW, et al. Clustering and modeling of EEG coherence features of Alzheimer′s and mild cognitive impairment patients [C] //Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver: IEEE, 2008: 1092-1095. [47] Akrofi K, Pal R, Baker MC, et al. Classification of Alzheimer′s disease and mild cognitive impairment by pattern recognition of EEG power and coherence [C] // 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing. Dallas: IEEE, 2010: 606-609. [48] Vialatte FB, Cichocki A, Dreyfus G, et al. Early detection of Alzheimer′s disease by blind source separation, time frequency representation, and bump modeling of EEG signals [C] //Artificial Neural Networks: Biological Inspirations-ICANN 2005. Warsaw: Springer, 2005: 683-692. [49] Laskaris NA, Tarnanas I, Tsolaki MN, et al. Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses [J]. J Neurosci Meth, 2013, 212(2): 344-354. [50] Michalopoulos K, Zervakis M, Deiber MP, et al. Classification of EEG single trial microstates using local global graphs and discrete Hidden Markov models [J]. Int J Neural Syst, 2016, 26(6): 1650036. [51] Rodrigues PM, Teixeira JP, Garrett C, et al. Alzheimer′s Early Prediction with Electroencephalogram [C] //Procedia Computer Science. Porto: Elsevier B.V, 2016: 865-871. [52] Buscema M, Grossi E, Capriotti M, et al. The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy [J]. Curr Alzheimer Res, 2010, 7(2): 173-187. [53] Rossini PM, Buscema M, Capriotti M, et al. Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? [J]. Clin Neurophysiol, 2008, 119(7): 1534-1545. [54] Morabito FC, Campolo M, Ieracitano C, et al. Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer′s Disease patients from scalp EEG recordings [C] // 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI). Bologna: IEEE, 2016: 1-6. [55] Song Yuedong, Crowcroft J, Zhang Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine [J]. J Neurosci Meth, 2012, 210(2): 132-146. [56] Yuan Qi, Zhou Weidong, Li Shufang, et al. Epileptic EEG classification based on extreme learning machine and nonlinear features [J]. Epilepsy Res, 2011, 96(1-2): 29-38. [57] Li Guoqiang, Niu Peifeng, Duan Xiaolong, et al. Fast learning network: A novel artificial neural network with a fast learning speed [J]. Neural Comput Appl, 2014, 24(7-8): 1683-1695. [58] Buscema M, Vernieri F, Massini G, et al. An improved I-FAST system for the diagnosis of Alzheimer′s disease from unprocessed electroencephalograms by using robust invariant features [J]. Artif Intell Med, 2015, 64(1): 59-74.
