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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

覃国萍, 李双燕, 徐桂芝. 多尺度熵算法研究进展及其在神经信号分析中的应用[J]. 仁和测试, 2020, 37(3): 541-548. doi: 10.7507/1001-5515.201908044
引用本文: 覃国萍, 李双燕, 徐桂芝. 多尺度熵算法研究进展及其在神经信号分析中的应用[J]. 仁和测试, 2020, 37(3): 541-548. doi: 10.7507/1001-5515.201908044
Guoping QIN, Shuangyan LI, Guizhi XU. Research progress on multiscale entropy algorithm and its application in neural signal analysis[J]. Rhhz Test, 2020, 37(3): 541-548. doi: 10.7507/1001-5515.201908044
Citation: Guoping QIN, Shuangyan LI, Guizhi XU. Research progress on multiscale entropy algorithm and its application in neural signal analysis[J]. Rhhz Test, 2020, 37(3): 541-548. 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
基金项目: 国家自然科学基金资助项目(51507047,51737003);河北省自然科学基金资助项目(F2018202319)

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

Funds: National Natural Science Foundation of China; Natural Science Foundation of Hebei Province
  • 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。
  • 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 *** * **** + +
    ${\rm{RCmvMF}}{{\rm{E}}_{{\sigma ^2}}}$ 2017 *** * *** + +
    mvMDE 2019 *** * * + +
    “*”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
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  • 收稿日期:  2019-08-21
  • 修回日期:  2020-03-03
  • 刊出日期:  2020-03-17

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