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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Retrieved from ” https: A time series containing many repetitive patterns has a relatively small ; a less predictable i. ApEn reflects complexiry likelihood that similar patterns of observations will not be followed by additional similar observations.

Two patterns, andare similar if the difference between any pair of corresponding measurements in the patterns is less thani. This step might cause bias of ApEn and this bias causes ApEn to have two poor properties in practice: Heart and Circulatory Physiology. 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.

This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart ebtropy, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency.

The advantages of ApEn include: Tapia CortezJames W. Pincus Published in Chaos Approximate entropy ApEn is a recently developed statistic quantifying regularity and complexity, which appears to have potential application to a wide variety of relatively short greater than points and noisy time-series data.


Thus, for example, is not similar tosince their last components 61 and 65 differ by dntropy than 2 units. The conditions aas similarity to will be satisfied only by, AhearnApprozimate D. Nor will rank order statistics distinguish between these series. Finally, we calculate that. Doing so, we obtain: Yet series 1 is “perfectly regular”; knowing one term has the value of 20 enables one to predict with certainty that the next term will have the value of The results using compound measures of behavioural patterns of fifteen healthy individuals are presented.

Entropy, Complexity and Stability. Views Read Edit View history.

SokunbiGeorge G. Scientific Research An Academic Publisher. Approximate measire as a measure of system complexity. Smaller values of imply a greater likelihood that similar patterns of measurements will be followed by additional similar measurements.

Approximate entropy (ApEn) as a complexity measure.

Citations Publications citing this paper. From This Paper Topics from this paper. 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. We can calculate for each pattern inand we define as the mean of these values.

This paper has highly influenced 51 other papers. The quantity is the fraction of patterns of length that resemble the meassure of the same length that begins at interval.

Topics Discussed in This Paper.

ApEn was developed by Steve M. Updated Thursday, 9 July at The algorithm for computing has been published elsewhere []. Series 2 is randomly valued; knowing one term has the value of 20 gives no insight into what value the next term will have.

Approximate entropy – Wikipedia

It should be noted that has significant weaknesses, notably its strong dependence on sequence length measude its poor self-consistency i. The second of these parameters,specifies the pattern length, and the third,defines the criterion of similarity. If the time series is highly irregular, the occurrence of similar patterns will not be predictive for the following measurements, and will be relatively large. Are women more complex than comppexity Hence is either ordepending onand the mean value of all 46 of the is: While a concern for artificially constructed examples, it is usually not a concern in practice.

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Since the total number of is. Journal of Clinical Monitoring and Computing. The development of ApEn was motivated by data length constraints alproximate encountered, e.

Approximate Entropy (ApEn)

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. Now consider the set of all patterns of length [i. From Wikipedia, the free encyclopedia. Suppose thatand that the sequence consists of 50 samples of the function illustrated above: 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 notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. Approximate entropy ApEn as a complexity measure. The application of the compound measures is shown to correlate with complexity analysis.

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