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Exploring stability of entropy analysis for signal with different trends

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  • Zhang, Yin
  • Li, Jin
  • Wang, Jun

Abstract

Considering the effects of environment disturbances and instrument systems, the actual detecting signals always are carrying different trends, which result in that it is difficult to accurately catch signals complexity. So choosing steady and effective analysis methods is very important. In this paper, we applied entropy measures—the base-scale entropy and approximate entropy to analyze signal complexity, and studied the effect of trends on the ideal signal and the heart rate variability (HRV) signals, that is, linear, periodic, and power-law trends which are likely to occur in actual signals. The results show that approximate entropy is unsteady when we embed different trends into the signals, so it is not suitable to analyze signal with trends. However, the base-scale entropy has preferable stability and accuracy for signal with different trends. So the base-scale entropy is an effective method to analyze the actual signals.

Suggested Citation

  • Zhang, Yin & Li, Jin & Wang, Jun, 2017. "Exploring stability of entropy analysis for signal with different trends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 60-67.
    • Handle: RePEc:eee:phsmap:v:470:y:2017:i:c:p:60-67
    DOI: 10.1016/j.physa.2016.11.073
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    References listed on IDEAS

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    2. Li, Yu & Wang, Jun & Li, Jin & Liu, Dazhao, 2015. "Effect of extreme data loss on heart rate signals quantified by entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 651-658.
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