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Using the modified sample entropy to detect determinism |
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| Authors: | Hong-Bo Xie Jing-Yi Guo |
| Institution: | a Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b Reseach Institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong c Department of Biomedical Engineering, Jiangsu University, Zhenjiang, PR China |
| Abstract: | A modified sample entropy (mSampEn), based on the nonlinear continuous and convex function, has been proposed and proven to be superior to the standard sample entropy (SampEn) in several aspects. In this Letter, we empirically investigate the ability of the mSampEn statistic combined with surrogate data method to detect determinism. The effects of the datasets length and noise on the proposed method to differentiate between deterministic and stochastic dynamics are tested on several benchmark time series. The noise performance of the mSampEn statistic is also compared with the singular value decomposition (SVD) and symplectic geometry spectrum (SGS) based methods. The results indicate that the mSampEn statistic is a robust index for detecting determinism in short and noisy time series. |
| Keywords: | Deterministic chaos Stochastic process Sample entropy Surrogate data Time series |
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