Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

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作者：Hamed Azami ; ^{hamed.azami@ed.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Javier Escudero ^{javier.escudero@ed.ac.uk" class="auth_mail" title="E-mail the corresponding author}
关键词：Complexity ; Multivariate generalized multiscale entropy ; Statistical moments ; Fuzzy entropy ; Sample entropy ; Biomedical signals
刊名：Physica A: Statistical Mechanics and its Applications
出版年：2017
出版时间：1 January 2017
年：2017
卷：465
期：Complete
页码：261-276
全文大小：965 K

文摘

class="label">•: We propose refined composite multivariate multiscale fuzzy entropy (RCmvMFE).
class="label">•: The coarse-graining step of RCmvMFE uses variance (RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si98.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=6c2a18513051787f9e74709cdef27912" title="Click to view the MathML source">_σ²class="mathContainer hidden">class="mathCode"> $_{σ^{2}}$ ) or mean (RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si99.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=a693ecbf6f72ed21af9613af4a0d15d0" title="Click to view the MathML source">_μclass="mathContainer hidden">class="mathCode"> $_{μ}$ ).
class="label">•: The introduced fuzzy membership function significantly decreases the running time.
class="label">•: Our simulations demonstrate that RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si98.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=6c2a18513051787f9e74709cdef27912" title="Click to view the MathML source">_σ²class="mathContainer hidden">class="mathCode"> $_{σ^{2}}$ and RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si99.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=a693ecbf6f72ed21af9613af4a0d15d0" title="Click to view the MathML source">_μclass="mathContainer hidden">class="mathCode"> $_{μ}$ lead to more stable results.
class="label">•: RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si98.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=6c2a18513051787f9e74709cdef27912" title="Click to view the MathML source">_σ²class="mathContainer hidden">class="mathCode"> $_{σ^{2}}$ and RCmvMFEclass="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0378437116305404&_mathId=si99.gif&_user=111111111&_pii=S0378437116305404&_rdoc=1&_issn=03784371&md5=a693ecbf6f72ed21af9613af4a0d15d0" title="Click to view the MathML source">_μclass="mathContainer hidden">class="mathCode"> $_{μ}$ are less sensitive to the length of signals.

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