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Multi-scale incremental entropy algorithm for evaluation of time sequence complexity

A technology of time series and complexity, applied in computing, applications, computer components, etc., can solve problems such as low computational efficiency, ignoring the natural time sequence of the sequence, and not considering the fluctuation range of adjacent points in the sequence, so as to achieve a small and consistent evaluation error. The effect of good performance and fast calculation time

Inactive Publication Date: 2019-03-29
HOHAI UNIV CHANGZHOU
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Problems solved by technology

Although sample entropy is often used to analyze the complexity of biological signals and has good accuracy and robustness, it ignores the natural timing of the sequence and has low computational efficiency.
The permutation entropy is an algorithm that evaluates the complexity of a sequence based on the natural timing of the sequence, but does not consider the fluctuation range of adjacent points of the sequence

Method used

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  • Multi-scale incremental entropy algorithm for evaluation of time sequence complexity
  • Multi-scale incremental entropy algorithm for evaluation of time sequence complexity
  • Multi-scale incremental entropy algorithm for evaluation of time sequence complexity

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0033] A multi-scale incremental entropy algorithm for time series complexity evaluation, the flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0034] (1) Set parameters, including scale Scale, embedding dimension m and resolution R;

[0035] (2) The scale is initialized to τ=1;

[0036] (3) Coarse-grain the original sequence to obtain a coarse-grained sequence at this scale;

[0037] (4) Calculate the incremental entropy of the coarse-grained sequence;

[0038] (5) scale τ plus 1;

[0039] (6) If the scale τ is smaller than the set scale Scale, repeat steps (3) to (5) until the corresponding scale Scale is reached; finally, the incremental entropy ...

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Abstract

The invention discloses a multi-scale incremental entropy algorithm for evaluation of time sequence complexity. For an original electrophysiological time sequence, the original sequence is firstly coarse-grained to obtain coarse-grained sequences in corresponding scale; then the incremental entropy of each coarse-grained sequence is calculated to obtain the incremental entropy value of the sequence at the scale, so the fluctuation of the signal complexity with the scale is calculated, and the characteristic of the signal is extracted. With the increase of the time scale, the evaluation error of the entropy value is small, the consistency of the algorithm is good, and the calculation time is short. The shortcomings of increasing of the original multi-scale entropy with time scale, decreasing of the data length and the undefined entropy value are overcome. The method has better recognition performance on two signals than multi-scale sample entropy.

Description

technical field [0001] The invention relates to a multi-scale incremental entropy algorithm for time series complexity evaluation, belonging to the technical field of nonlinear signal analysis. Background technique [0002] Physiological signals contain nonlinear dynamic effects on multiple time scales. Entropy is a method to evaluate the complexity of nonlinear signals. The more commonly used entropy are: approximate entropy, sample entropy and permutation entropy. However, these entropy algorithms do not take into account the multi-scale characteristics of the sequence as the time scale changes, and the multi-scale entropy makes up for this defect. The original multi-scale entropy algorithm is based on the sample entropy algorithm to extract multi-scale features of the signal. Although sample entropy is often used to analyze the complexity of biological signals and has good accuracy and robustness, it ignores the natural timing of the sequence and has low computational e...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/024A61B5/00G06K9/62
CPCA61B5/024A61B5/02405A61B5/72G06F18/211
Inventor 刘小峰王雪
Owner HOHAI UNIV CHANGZHOU
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