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

Guoping QIN; Shuangyan LI; Guizhi XU

doi:10.7507/1001-5515.201908044

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Mar 2020

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JOURNAL OF MECHANICAL ENGINEERING > 2020 > 37(3): 541-548.

Guoping QIN, Shuangyan LI, Guizhi XU. Research progress on multiscale entropy algorithm and its application in neural signal analysis[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 541-548. doi: 10.7507/1001-5515.201908044

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Guoping QIN, Shuangyan LI, Guizhi XU. Research progress on multiscale entropy algorithm and its application in neural signal analysis[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 541-548. doi: 10.7507/1001-5515.201908044

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

doi: 10.7507/1001-5515.201908044

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

Received Date: 21 Aug 2019
Rev Recd Date: 03 Mar 2020
Publish Date: 17 Mar 2020

Abstract

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

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References(52)

References

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