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- W2784605619 abstract "Symbolization is prerequisite for symbolic time series analysis which is important in nonlinear dynamic complexity analysis. Shannon entropy for a parameter-dependent four-symbols transformation is analyzed and its complexity detection is validated in our works. We analyze the symbolic entropy in complexity detection of two chaotic models, logistic and Henon maps, and use ECG and HRV derived from ECG of CHF patients, healthy young and elderly subjects to test the impacts of parameter settings on complexity analysis. The complexity-loss theory of aging and diseased heartbeats is validated and reasons that may account for the paradox of symbolic entropy in ECG are discussed. Tests results prove that original parameter settings are not universally suitable for different time series, and it is necessary to adjust the controlling parameter accordingly." @default.
- W2784605619 created "2018-02-02" @default.
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- W2784605619 date "2018-01-24" @default.
- W2784605619 modified "2023-09-27" @default.
- W2784605619 title "On a parameter-dependent symbolic entropy in nonlinear dynamics complexity detection" @default.
- W2784605619 hasPublicationYear "2018" @default.
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