Pincus, S. () Approximate Entropy (ApEn) as a Complexity Measure. Chaos, 5, APPROXIMATE ENTROPY: A COMPLEXITY MEASURE FOR. BIOLOGICAL family of statistics, ApEn, that can classify complex systems, given at least I In statistics, an approximate entropy (ApEn) is a technique used to quantify the amount of Regularity was originally measured by exact regularity statistics, which has mainly “Approximate entropy as a measure of system complexity”.

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This paper has highly influenced 51 other papers. The advantages of ApEn include: Heart and Circulatory Physiology.

Approximate entropy (ApEn) as a complexity measure. – Semantic Scholar

In statisticsan approximate entropy ApEn is a technique used to quantify the amount of regularity and the unpredictability of fluctuations over time-series data.

A time series containing many repetitive patterns has a relatively small ; a less predictable i. American Journal of Physiology. Thus, for example, is not similar tosince their last components 61 and 65 differ by more than 2 units.

Approximate entropy (ApEn) as a complexity measure.

By using this site, you agree to the Terms of Use and Privacy Policy. Determining the chaotic behaviour of copper prices in the long-term using annual price data C. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented.


Entropy, Complexity and Stability. ApEn was developed by Steve M. The application of the compound measures is shown to correlate with complexity analysis.

Pincus to handle these limitations by modifying an exact regularity statistic, Kolmogorov—Sinai entropy.

Approximate Entropy (ApEn)

Journal of Clinical Monitoring and Computing. GrandyDouglas D. This description is provided here so that researchers who wish to use ApEn can write their own code for doing so.

AhearnAlison D. Smaller values of imply a greater likelihood that similar patterns of measurements will be followed by additional similar measurements.

Doing so, we obtain: Thus, if we find similar patterns in a heart rate time series, estimates the logarithmic likelihood that the next intervals after each of the patterns will differ i. Hidden Information, Energy Dispersion and Disorder: Here, we provide a brief summary of the calculations, as applied to a time series of heart rate measurements. Since we have chosen as the similarity criterion, this means that each of the 5 components of must be within units of the corresponding component of.

While a concern for artificially constructed examples, it is usually not a concern in practice. Hence is either ordepending onand the mean value of all 46 of the is: A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. The correlation is demonstrated using two healthy subjects compared against a control group. From Wikipedia, the free encyclopedia. Regularity complexigy originally measured by exact regularity statistics, which has mainly centered on various entropy measures.


Approximate Entropy (ApEn)

Since the total number of is. Aa second of these parameters,specifies the pattern length, and the third,defines the criterion of similarity. Intuitively, one may reason that the presence of repetitive patterns of fluctuation in a time series renders it more predictable than a time series in which such patterns are absent.

Let’s choose this choice simplifies the calculations for this example, but similar results would be obtained for other nearby values of and again, the value of can be varied somewhat without affecting the result.

For an excellent review of the shortcomings of and the strengths of alternative statistics, see reference [5]. This is a very small value of ApEn, emtropy suggests that the original time series is highly predictable compledity indeed it is. By the same reasoning, is similar to, ,