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摘要: 面向孤独症儿童脑功能状态评估问题, 提出一种多重多尺度熵脑电特征提取算法.算法针对传统多尺度熵信息丢失问题, 在移动均值粗粒化基础上, 采用延搁取值法构建多个尺度上的多重脑电信号序列, 再进一步计算各个尺度的样本熵.算法不仅克服了传统多尺度熵的信息丢失问题, 还能充分挖掘脑电信号的细节信息, 同时减小了尺度间的波动.基于该算法分析了16名孤独症儿童和16名正常儿童的19个通道的脑电信号.结果表明:正常儿童F7、F8、T4、P3通道的多重多尺度熵和复杂度均高于孤独症儿童, 且存在显著性差异(P < 0.05).表明前颞叶(F7、F8)可以作为孤独症儿童脑功能状态评估的敏感脑区, T4、P3可以作为辅助干预的敏感通道.Abstract: To focus on the assessment of brain functional status of autism spectrum disorders (ASD), an electroencephalogram (EEG) feature extraction algorithm of multiple multi-scale entropies is proposed in this paper. In order to solve the problem of losing EEG information by traditional multi-scales entropy (MSE), moving averaging (MA) coarse graining is done first in the multiple multi-scale entropy algorithm, then multiple scale EEG signals are built using delay value method, before the sample entropy of each scale is calculated. The algorithm not only overcomes the information loss problem of traditional multi-scale entropy, but also fully excavates details of the EEG and reduces fluctuations between the scales. Based on this algorithm, 19 channels of EEG signals with 16 autistic children and 16 normal children are analyzed, and the result shows that multiple multi-scale entropies of normal children are higher those of children with autism in channels F7, F8, T4, P3 by a significant difference (P < 0.05). It is suggested that the anterior temporal lobe (F7 and F8) should be used as a sensitive brain area for evaluating the brain function of autistic children, and T4 and P3 as sensitive channels for auxiliary and intervention.
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Key words:
- Autism /
- resting electroencephalogram (EEG) /
- multiple multiscale entropies /
- complexity
1) 本文责任编委 许斌 -
图 9 多重多尺度熵与传统多尺度熵脑电信号结果对比
Fig. 9 EEG comparison of traditional multiscale entropy and multiple multiscale entropy
表 1 孤独症与正常儿童复杂度显著性检验结果
Table 1 Autistic children with normal complexity signiflcant test results
通道名称 显著性(P值) 通道名称 显著性(P值) FP1 0.009 FP2 0.134 F3 0.094 F4 0.067 F7 0.003 F8 0.028 T3 0.001 T4 0.001 T5 0.032 T6 0.019 C3 0.041 C4 0.080 P3 0.003 P4 0.019 O1 0.036 O2 0.079 
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表 2 孤独症与正常儿童多重多尺度熵显著性检验结果
Table 2 Autistic and normal children multiple multiscale entropy significant test results
通道名称 显著性(P值) 通道名称 显著性(P值) F7 0.001 F8 0.017 T3 0.148 T4 0.001 P3 0.001 P4 0.060
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[1] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (5th edition). USA: American Psychiatric Publishing, 2013. [2] Saemundsen E, Magnússon P, Georgsdóttir I, Egilsson E, Rafnsson V. Prevalence of autism spectrum disorders in an Icelandic birth cohort. BMJ Open, 2013, 3(6): e002748 doi: 10.1136/bmjopen-2013-002748 [3] Wang J, Barstein J, Ethridge L E, Mosconi M W, Takarae Y, Sweeney J A. Resting state EEG abnormalities in autism spectrum disorders. Journal of Neurodevelopmental Disorders, 2013, 5(1): 1-14 doi: 10.1186/1866-1955-5-1 [4] Sutton S K, Burnette C P, Mundy P C, Meyer J, Vaughan A, Sanders C, et al. Resting cortical brain activity andsocial behavior in higher functioning children with autism. Journal of Child Psychology and Psychiatry, 2005, 46(2): 211-222 doi: 10.1111/j.1469-7610.2004.00341.x [5] Sheikhani A, Behnam H, Mohammadi M R, Noroozian M, Mohammadi M. Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroencephalography analysis. Journal of Medical Systems, 2012, 36(2): 957-963 doi: 10.1007/s10916-010-9560-6 [6] Fan Y M, Zeng L L, Shen H, Qin J, Li F Q, Hu D W. Lifespan development of the human brain revealed by large-scale network eigen-entropy. Entropy, 2017, 19(9): 471 doi: 10.3390/e19090471 [7] Song Y D, Crowcroft J, Zhang J X. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of Neuroscience Methods, 2012, 210(2): 132-146 doi: 10.1016/j.jneumeth.2012.07.003 [8] Nicolaou N, Georgiou J. Detection of epileptic electroencephalogram based on Permutation entropy and support vector machines. Expert Systems with Applications, 2012, 39(1): 202-209 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=deeb57a35ad92924a4c9025ccccd3ca3 [9] 雷敏, 孟光, 张文明, Sarkar N.基于虚拟开车环境的自闭症儿童脑电样本熵.物理学报, 2016, 65(10): 108701 doi: 10.7498/aps.65.108701Lei Min, Meng Guang, Zhang Wen-Ming, Sarkar N. Sample entropy of electro encephalogram for children with autism based on virtual driving game. Acta Physica Sinica, 2016, 65(10): 108701 doi: 10.7498/aps.65.108701 [10] Lei M, Meng G, Zhang W M, Wade J, Sarkar N. Symplectic entropy as a novel measure for complex systems. Entropy, 2016, 18(11): 412 doi: 10.3390/e18110412 [11] Bornas X, Llabrés J, Noguera M, López A M, Gelabert J M, Vila I. Fear induced complexity loss in the electrocardiogram of flight phobics: a multiscale entropy analysis. Biological Psychology, 2006, 73(3): 272-279 doi: 10.1016/j.biopsycho.2006.05.004 [12] Thuraisingham R A, Gottwald G A. On multiscale entropy analysis for physiological data. Physica A: Statistical Mechanics and Its Applications, 2006, 366(1): 323-332 http://www.maths.usyd.edu.au/u/gottwald/preprints/MultiscaleEntropy.pdf [13] Zavala-Yoé R, Ramírez-Mendoza R, Cordero L M. Novel way to investigate evolution of children refractory epilepsy by complexity metrics in massive information. SpringerPlus, 2015, 4(1): 437 doi: 10.1186/s40064-015-1173-6 [14] Mclntosh A R, Kovacevic N, Itier R J. Increased brain signal variability accompanies lower behavioral variability in development. PLoS Computational Biology, 2008, 4(7): e1000106 doi: 10.1371/journal.pcbi.1000106 [15] Bosl W, Tierney A, Tager-Flusberg H, Nelson C. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 2011, 9(1): 18 doi: 10.1186/1741-7015-9-18 [16] Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clinical Neurophysiology, 2011, 122(12): 2375-2383 doi: 10.1016/j.clinph.2011.05.004 [17] Zhao M Y, Xu G. Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy. IET Science, Measurement and Technology, 2018, 12(1): 63-71 doi: 10.1049/iet-smt.2017.0188 [18] 高军峰, 司慧芳, 余彬, 顾凌云, 梁莹, 杨勇.基于脑电样本熵的测谎分析.电子学报, 2017, 45(8): 1836-1841 doi: 10.3969/j.issn.0372-2112.2017.08.005Gao Jun-Feng, Si Hui-Fang, Yu Bin, Gu Ling-Yun, Liang Ying, Yang Yong. Lie detection analysis based on the sample entropy of EEG. Acta Electronica Sinica, 2017, 45(8): 1836-1841 doi: 10.3969/j.issn.0372-2112.2017.08.005 [19] 刘志勇, 孙金玮, 卜宪庚.单通道脑电信号眼电伪迹去除算法研究.自动化学报, 2017, 43(10): 1726-1735 doi: 10.16383/j.aas.2017.c160191Liu Zhi-Yong, Sun Jin-Wei, Bu Xian-Geng. EOG artifact removing method for single-channel EEG signal. Acta Automatica Sinica, 2017, 43(10): 1726-1735 doi: 10.16383/j.aas.2017.c160191 [20] Kaspar F, Schuster H G. Easily calculable measure for the complexity of spatiotemporal patterns. Physical Review A, 1987, 36(2): 842-848 doi: 10.1103/PhysRevA.36.842 [21] Marwaha P, Sunkaria R K. Complexity quantification of cardiac variability time series using improved sample entropy (I-SampEn). Australasian Physical and Engineering Sciences in Medicine, 2016, 39(3): 755-763 doi: 10.1007/s13246-016-0457-7 [22] 张毅, 尹春林, 蔡军, 罗久飞. Bagging RCSP脑电特征提取算法.自动化学报, 2017, 43(11): 2044-2050 doi: 10.16383/j.aas.2017.c160094Zhang Yi, Yin Chun-Lin, Cai Jun, Luo Jiu-Fei. Bagging RCSP algorithm for extracting EEG feature. Acta Automatica Sinica, 2017, 43(11): 2044-2050 doi: 10.16383/j.aas.2017.c160094 [23] 王金甲, 陈春.分层向量自回归的多通道脑电信号的特征提取研究.自动化学报, 2016, 42(8): 1215-1226 doi: 10.16383/j.aas.2016.c150461Wang Jin-Jia, Chen Chun. Multi-channel EEG feature extraction using hierarchical vector autoregression. Acta Automatica Sinica, 2016, 42(8): 1215-1226 doi: 10.16383/j.aas.2016.c150461 [24] Sheikhani A, Behnam H, Mohammadi M R, Noroozian M, Golabi P. Analysis of quantitative electroencephalogram background activity in autism disease patients with Lempel-Ziv complexity and short time fourier transform measure. In: Proceedings of the 4th IEEE/EMBS International Summer School and Symposium on Medical Devices and Biosensors. Cambridge, UK: IEEE, 2007. 111-114 [25] Jaime M, McMahon C M, Davidson B C, Newell L C, Mundy P C, Henderson H A. Brief report: reduced temporal-central EEG alpha coherence during joint attention perception in adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 2016, 46(4): 1477 -1489 doi: 10.1007/s10803-015-2667-3 [26] Greimel E, Nehrkorn B, Schulte-Rüther M, Fink G R, Nickl-Jockschat T, Herpertz-Dahlmann B, et al. Changes in grey matter development in autism spectrum disorder. Brain Structure and Function, 2013, 218(4): 929-942 doi: 10.1007/s00429-012-0439-9 [27] Ecker C, Suckling J, Deoni S C, Lombardo M V, Bullmore E T, Baron-Cohen S, et al. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. Archives of General Psychiatry, 2012, 69(2): 195-209 doi: 10.1001/archgenpsychiatry.2011.1251 -
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