多尺度熵算法研究进展及其在神经信号分析中的应用

覃国萍; 李双燕; 徐桂芝

doi:10.7507/1001-5515.201908044

多尺度熵算法研究进展及其在神经信号分析中的应用

doi: 10.7507/1001-5515.201908044

覃国萍^{1, 2},
李双燕^{1, 2,},
徐桂芝^{1, 2}

计量
- 文章访问数: 907
- HTML全文浏览量: 265
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2019-08-21
- 修回日期: 2020-03-03
- 发布日期: 2020-03-17

Research progress on multiscale entropy algorithm and its application in neural signal analysis

Guoping QIN^{1, 2},
Shuangyan LI^{1, 2
,},
Guizhi XU^{1, 2}

摘要

摘要: 脑神经电生理信号的内在特征变化能够反映脑功能的正常与否，因此有效的特征提取分析方法有利于脑功能异常的早期诊断与相关疾病的治疗。近年来的研究表明，神经电信号具有非线性和多尺度的特性。基于此，科研人员近来发展了适用于多尺度非线性信号分析的多尺度熵（MSE）算法，并在神经信息科学领域得到了广泛应用。本文对 MSE 算法的原理和特性进行了介绍，并进一步介绍了几种在实际应用中针对 MSE 算法的一些不足而提出的相关改进算法。然后，对 MSE 及其改进算法在疾病诊断、脑功能分析以及脑-机接口等方面的应用进行了综述。最后，对上述各算法在神经信号分析中面临的挑战及其可能的发展方向进行了探讨。
- 多尺度熵 /
- 多尺度熵的改进算法 /
- 神经信号分析
Abstract: Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.
- multi-scale entropy /
- improved algorithm of multi-scale entropy /
- neural signal analysis

HTML全文

表 1 MSE 及相关算法特点

Table 1. The characteristics of the MSE and the related algorithms

算法	出现时间	精确度	不确定熵	信息丢失	计算量	多变量数据	短时序列
MSE	2002	*	**	***	*	−	−
MMSE	2012	*	**	***	**	+	−
ModMSE	2013	**	*	**	*	−	+
CMSE	2013	**	***	***	**	−	−
RCMSE	2014	***	*	*	***	−	+
MRCMSE	2016	***	*	*	****	+	+
${\rm{RCmvMF}}{{\rm{E}}_\mu }$	2017	***	无	*	****	+	+
${\rm{RCmvMF}}{{\rm{E}}_{{\sigma ^2}}}$	2017	***	无	*	***	+	+
mvMDE	2019	***	无	*	*	+	+
“*”数量的多少代表程度的大小，注意此表示方法是定性描述，而非定量分析；“+”代表方法有效，“−”代表方法无效或效果很差。

下载: 导出CSV

参考文献(52)

[1]	Baumert M, Porta A, Cichocki A. Biomedical signal processing: from a conceptual framework to clinical applications. Proceedings of the IEEE, 2016, 104(2): 213-214. doi: 10.1109/JPROC.2016.2517929
[2]	Liu Yunxiao, Lin Youfang, Wang Jing, et al. Refined generalized multiscale entropy analysis for physiological signals. Physica A: Statistical Mechanics and its Applications, 2018, 490: 975-985. doi: 10.1016/j.physa.2017.08.047
[3]	Mateos D M, Guevara E R, Wennberg R, et al. Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn, 2018, 12(1): 73-84. doi: 10.1007/s11571-017-9459-8
[4]	Gao Zhongke, Zhang Jun, Dang Weidong, et al. Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system. EPL, 2018, 122(4): 40010. doi: 10.1209/0295-5075/122/40010
[5]	张汉勇, 孟庆芳, 杜蕾, 等. 基于加权复杂网络度熵和的癫痫发作检测方法. 中国生物医学工程学报, 2019, 38(3): 273-280. doi: 10.3969/j.issn.0258-8021.2019.03.003
[6]	Cao Zehong, Lin C T. Inherent fuzzy entropy for the improvement of EEG complexity evaluation. IEEE Transactions on Fuzzy Systems, 2018, 26(2): 1032-1035. doi: 10.1109/TFUZZ.2017.2666789
[7]	Mesin L. Estimation of complexity of sampled biomedical continuous time signals using approximate entropy. Front Physiol, 2018, 9: 1-15. doi: 10.3389/fphys.2018.00001
[8]	Sun Rui, Wong W W, Wang Jing, et al. Changes in electroencephalography complexity using a brain computer interface-motor observation training in chronic stroke patients: a fuzzy approximate entropy analysis. Front Hum Neurosci, 2017, 11: 1-13.
[9]	Kang Jiannan, Chen Huimin, Li Xin, et al. EEG entropy analysis in autistic children. Journal of Clinical Neuroscience, 2019, 62: 199-206. doi: 10.1016/j.jocn.2018.11.027
[10]	Costa M, Goldberger A L, Peng C K. Multiscale entropy to distinguish physiologic and synthetic RR time series//29th Computers in Cardiology (CinC), Memphis: IEEE, 2002, 29: 137-140.
[11]	Kaur Y, Ouyang Guang, Junge M, et al. The reliability and psychometric structure of multi-scale entropy measured from EEG signals at rest and during face and object recognition tasks. J Neurosci Methods, 2019, 326: 108343. doi: 10.1016/j.jneumeth.2019.108343
[12]	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049. doi: 10.1152/ajpheart.2000.278.6.H2039
[13]	Ahmed M U, Mandic D P. Multivariate multiscale entropy analysis. IEEE Signal Process Lett, 2012, 19(2): 91-94. doi: 10.1109/LSP.2011.2180713
[14]	Wu S D, Wu C W, Lee K Y, et al. Modified multiscale entropy for short-term time series analysis. Physica A: Statistical Mechanics and its Applications, 2013, 392(23): 5865-5873. doi: 10.1016/j.physa.2013.07.075
[15]	Wu S D, Wu C W, Lin S G, et al. Time series analysis using composite multiscale entropy. Entropy, 2013, 15(3): 1069-1084. doi: 10.3390/e15031069
[16]	Wu S D, Wu C W, Lin S G, et al. Analysis of complex time series using refined composite multiscale entropy. Phys Lett A, 2014, 378(20): 1369-1374. doi: 10.1016/j.physleta.2014.03.034
[17]	Humeau-Heurtier A. Multivariate refined composite multiscale entropy analysis. Phys Lett A, 2016, 380(16): 1426-1431. doi: 10.1016/j.physleta.2016.02.029
[18]	Humeau-Heurtier A. Multivariate generalized multiscale entropy analysis. Entropy, 2016, 18(11): 411. doi: 10.3390/e18110411
[19]	Azami H, Escudero J. Refined composite multivariate generalized multiscale fuzzy entropy: a tool for complexity analysis of multichannel signals. Physica A: Statistical Mechanics and its Applications, 2017, 465: 261-276. doi: 10.1016/j.physa.2016.07.077
[20]	Li Peng, Ji Lizhen, Yan Chang, et al. Coupling between short-term heart rate and diastolic period is reduced in heart failure patients as indicated by multivariate entropy analysis//41st Computing in Cardiology(CinC), Cambridge: IEEE, 2014, 41: 97-100.
[21]	Costa M, Goldberger A. Generalized multiscale entropy analysis: application to quantifying the complex volatility of human heartbeat time series. Entropy, 2015, 17(3): 1197-1203. doi: 10.3390/e17031197
[22]	Azami H, Rostaghi M, Abasolo D, et al. Refined composite multiscale dispersion entropy and its application to biomedical signals. IEEE Trans Biomed Eng, 2017, 64(12): 2872-2879. doi: 10.1109/TBME.2017.2679136
[23]	Azami H, Escudero J. Coarse-graining approaches in univariate multiscale sample and dispersion entropy. Entropy, 2018, 20(2): 138. doi: 10.3390/e20020138
[24]	Azami H, Kinney-Lang E, Ebied A, et al. Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo: IEEE, 2017: 3182-3185.
[25]	Rostaghi M, Azami H. Dispersion entropy: a measure for time-series analysis. IEEE Signal Process Lett, 2016, 23(5): 610-614. doi: 10.1109/LSP.2016.2542881
[26]	Azami H, Fernández A, Escudero J. Multivariate multiscale dispersion entropy of biomedical times series. Entropy, 2019, 21(9): 913. doi: 10.3390/e21090913
[27]	倪力, 曹建庭, 王如彬. 自适应多尺度熵在脑死亡诊断中的应用. 动力学与控制学报, 2014, 12(1): 74-78. doi: 10.6052/1672-6553-2013-086
[28]	李昕, 谢佳利, 侯永捷, 等. 改进的多尺度熵算法及其情感脑电特征提取性能分析. 高技术通讯, 2015, 25(10-11): 865-870.
[29]	Arunachalam S P, Kapa S, Mulpuru S K, et al. Improved multiscale entropy technique with nearest-neighbor moving-average kernel for nonlinear and nonstationary short-time biomedical signal analysis. J Healthc Eng, 2018, 2018: 1-13.
[30]	Silva L V, Duque J J, Felipe J C, et al. Two-dimensional multiscale entropy analysis: applications to image texture evaluation. Signal Processing, 2018, 147: 224-232. doi: 10.1016/j.sigpro.2018.02.004
[31]	李昕, 安占周, 李秋月, 等. 加权多重多尺度熵及其在孤独症儿童脑电信号分析中的应用. 生物医学工程学杂志, 2019, 36(1): 33-39, 49.
[32]	Ahmed M, Chanwimalueang T, Thayyil S, et al. A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis. Entropy, 2016, 19(1): 1-18. doi: 10.3390/e19010001
[33]	Michalopoulos K, Bourbakis N. Application of multiscale entropy on EEG signals for emotion detection//2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando: IEEE, 2017: 341-344.
[34]	Liu Tian, Chen Yanni, Chen Desheng, et al. Altered electroencephalogram complexity in autistic children shown by the multiscale entropy approach. Neuroreport, 2017, 28(3): 169-173. doi: 10.1097/WNR.0000000000000724
[35]	Hikmat H, Maha A, Enas A. Brain complexity in children with mild and severe autism spectrum disorders: analysis of multiscale entropy in EEG. Brain Topogr, 2019, 32(5): 914-921. doi: 10.1007/s10548-019-00711-1
[36]	Jaworska N, WANG Hongye, Smith D M, et al. Pre-treatment EEG signal variability is associated with treatment success in depression. Neuroimage Clin, 2018, 17: 368-377. doi: 10.1016/j.nicl.2017.10.035
[37]	Low I, Kuo P C, Liu Y H, et al. Altered brain complexity in women with primary dysmenorrhea: a resting-state magneto-encephalography study using multiscale entropy analysis. Entropy, 2017, 19(12): 680-707. doi: 10.3390/e19120680
[38]	范文兵, 刘雪峰, 赵艳阳. 基于单通道脑电信号的自动睡眠分期. 计算机应用, 2017, 37(s2): 318-321.
[39]	刘雪峰, 马州生, 赵艳阳, 等. 基于MSE-PCA的脑电睡眠分期方法研究. 电子技术应用, 2017, 43(9): 22-24, 29.
[40]	叶仙, 胡洁, 田畔, 等. 基于精细复合多尺度熵与支持向量机的睡眠分期. 上海交通大学学报, 2019, 53(3): 321-326.
[41]	Tian Pan, Hu Jie, Qi Jin, et al. A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture. Biocybernetics and Biomedical Engineering, 2017, 37(2): 263-271. doi: 10.1016/j.bbe.2017.01.005
[42]	王瑶, 黄国睿, 谢康宁. 脑电多尺度熵用于睡意检测的初步研究. 医疗卫生装备, 2016, 37(12): 1-6.
[43]	Sheehan T C, Sreekumar V, Inati S K, et al. Signal complexity of human intracranial EEG tracks successful associative-memory formation across individuals. J Neurosci, 2018, 38(7): 1744-1755. doi: 10.1523/JNEUROSCI.2389-17.2017
[44]	Wang Chunhao, Moreau D, Yang Chengta, et al. Aerobic exercise modulates transfer and brain signal complexity following cognitive training. Biol Psychol, 2019, 144: 85-98. doi: 10.1016/j.biopsycho.2019.03.012
[45]	Mcintosh A R. Neurocognitive aging and brain signal complexity. bioRxiv, 2018. DOI: https://doi.org/10.1101/259713.
[46]	Grundy J G, Barker R M, Anderson J E, et al. The relation between brain signal complexity and task difficulty on an executive function task. Neuroimage, 2019, 198: 104-113. doi: 10.1016/j.neuroimage.2019.05.045
[47]	Szostakiwskyj J M H, Willatt S E, Cortese F, et al. The modulation of EEG variability between internally- and externally-driven cognitive states varies with maturation and task performance. PLoS One, 2017, 12(7): e0181894. doi: 10.1371/journal.pone.0181894
[48]	Li Xuanyu, Zhu Zhaojun, Zhao Weina, et al. Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer’s disease: a multi-scale entropy analysis. Biomed Opt Express, 2018, 9(4): 1916-1929. doi: 10.1364/BOE.9.001916
[49]	Wang D J J, Jann K, Fan Chang, et al. Neurophysiological basis of multi-scale entropy of brain complexity and its relationship with functional connectivity. Front Neurosci, 2018, 12: 1-14. doi: 10.3389/fnins.2018.00001
[50]	邹晓红, 张轶勃, 孙延贞. 基于局部均值分解和多尺度熵的运动想象脑电信号特征提取方法. 高技术通讯, 2018, 28(1): 22-28. doi: 10.3772/j.issn.1002-0470.2018.01.004
[51]	Luo Haowen, Qiu Taorong, Liu Chao, et al. Research on fatigue driving detection using forehead EEG based on adaptive multi-scale entropy. Biomed Signal Process Control, 2019, 51: 50-58. doi: 10.1016/j.bspc.2019.02.005
[52]	Li Mingai, Wang Ruotu, Yang Jinfu, et al. An improved refined composite multivariate multiscale fuzzy entropy method for MI-EEG feature extraction. Comput Intell Neurosci, 2019, 2019: 1-12.