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dc.contributor.authorLu, Jue
dc.contributor.authorWang, Ze
dc.date.accessioned2021-06-09T18:40:15Z
dc.date.available2021-06-09T18:40:15Z
dc.date.issued2021-05-24
dc.identifier.urihttp://hdl.handle.net/10713/15972
dc.description.abstractEntropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the "logarithmic likelihood" that a small section (within a window of a length m) of the data "matches" with other sections will still "match" the others if the section window length increases by one. "Match" is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculationen_US
dc.description.urihttps://doi.org/10.3390/e23060659en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofEntropy (Basel, Switzerland)en_US
dc.subjectentropyen_US
dc.subjectmulti-scale sample entropyen_US
dc.subjectsystematic biasen_US
dc.titleThe Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/e23060659
dc.identifier.pmid34074036
dc.source.volume23
dc.source.issue6
dc.source.countrySwitzerland


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