Research progress on multiscale entropy algorithm and its application in neural signal analysis.
10.7507/1001-5515.201908044
- Author:
Guoping QIN
1
,
2
;
Shuangyan LI
1
,
2
;
Guizhi XU
1
,
2
Author Information
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China.
- Publication Type:Journal Article
- Keywords:
improved algorithm of multi-scale entropy;
multi-scale entropy;
neural signal analysis
- From:
Journal of Biomedical Engineering
2020;37(3):541-548
- CountryChina
- Language:Chinese
-
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.
