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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CameronTrevor S. Approximate entropy ApEn as a complexity measure.

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

This description originally appeared in slightly modified form, and without the example, in Ho, Moody, Peng, et al. It should be noted that has significant weaknesses, appriximate its strong dependence on sequence length and its poor self-consistency i. Applied MathematicsVol.

Finally, we calculate that. Time series Entropy and information. A time series containing many repetitive patterns has a relatively small ; w less predictable i. Journal of Clinical Monitoring and Computing. The ApEn algorithm counts each sequence as matching itself to avoid the occurrence of ln 0 in the calculations.

Approximate entropy (ApEn) as a complexity measure.

Since the total number of is. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures.


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.

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. Approximate entropy as a measure of system complexity.

Topics Discussed in This Paper. Showing of extracted citations. Hence is either ordepending onand the mean value of all 46 of the is: PuthankattilPaul K.

The results using compound measures of behavioural patterns of fifteen healthy individuals are presented.

MurrayRoger T. While a concern for artificially constructed examples, it is usually not a concern in practice. Citations Publications citing this paper. By the same reasoning, is similar to,The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin measre, movement and steps frequency. By using this site, you agree to the Terms of Use and Privacy Policy.

Approximate Entropy (ApEn)

J Am Coll Cardiol ; We can calculate for each pattern inand we define as the mean of these values. American Journal of Physiology.

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span. The correlation is demonstrated using two healthy subjects compared against a control group. The American Journal of Physiology. On the estimation of brain signal entropy from sparse neuroimaging data. Pincus to handle these limitations by modifying an exact regularity statistic, Kolmogorov—Sinai entropy. ApEn was initially developed to analyze medical data, such as heart rate, [1] and later spread its applications in finance[3] psychology[4] zpproximate human factors engineering.


Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. The development of ApEn was motivated by data length constraints commonly encountered, e. Updated Thursday, 9 July at Here, we provide a brief summary of the calculations, as applied to a time series of heart rate measurements.

Moment statisticssuch as mean and variancewill not compleexity between these two series.

Two patterns, andare similar if the difference between any pair of corresponding measurements in the patterns is less thani. Nor will rank order statistics distinguish com;lexity these series.