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A novel method for nonlinear detection of biomedical signal based on fuzzy entropy

Author(s): Cao Rui, Wang Huiqing, Deng Hongxia, Li Conggai, Chen Junjie

The nonlinearity of biomedical signals time series is detected by surrogate method. However, the traditional statistics in surrogate method, such as correlation dimension (D2) and approximate entropy (ApEn), have some insufficiency in application, especially lower time efficiency. To solve these deficiencies, this study presents the fuzzy entropy (FuzzyEn) as a statistics of the surrogate method to detect the nonlinearity of time series and verify that in two simulation datasets. It was found that, for various lengths of time series, the new method can accurately detect the linearity or nonlinearity of them, and perform much better in time efficiency compared with traditional statistics. The results show that, the method presented in this article is an accurate, effective method to detect the nonlinearity of the biomedical signal.

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