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Title: Παρουσίαση και εφαρμογές της προσεγγιστικής εντροπίας (ApEn) στην ιατρική
Presentation and applications of approximate entropy in medicine
Authors: Καρκαλούσος, Πέτρος
Δελίχα, Ε.
Item type: Journal article
Keywords: ApEn;Κανονικότητα;Προσεγγιστική εντροπία;Χρονοσειρά;Regularity;Approximate entropy;Time series
Subjects: Medicine
Issue Date: 6-May-2015
Date of availability: 6-May-2015
Language: Greek
Citation: Καρκαλούσος, Π. και Δελίχα, Ε. (2004) Παρουσίαση και εφαρµογές της προσεγγιστικής εντροπίας (ApEn) στην ιατρική. "Αρχεία Ελληνικής Ιατρικής", 21 (2), σ. 161-171. Διαθέσιμο στο: [Έγινε Πρόσβαση: 06/05/2015].
Journal: Αρχεία Ελληνικής Ιατρικής
Archives of Hellenic Medicine
Type of Journal: With a review process (peer review)
Table of contents: Approximate entropy (ApEn) is a real positive number which compares the regularity of different time-series of numbers always under certain circumstances. Such time-series can be the beats of EEG or even the daily secretion of hormones. Τhe algorithm of ApEn invented in 1991 by the American Steve Pincus and it has already been used for the compare between physiological and non-physiological reasons. In this article we’ll present concisely the most important applications of ApEn from the international bibliography. We will present also two relevant studies from our laboratory. The applications have to do with the evaluation of “suspicious” EEG and ECG, the study of pulsatile hormones secretion and other scientific fields such as the movements of breathing muscles. By mathematical point of view, approximate entropy states the possibility that the values of time-series are and stay within certain limits. These limits are the so-called filter of the algorithm (r). In practice the algorithm checks the differences between the values and not the values themselves because the target of ApEn is the check of regularity (complexity) of a system. The number of differences of each value which will be compared with the filter of the algorithm are the so-called “window” of the method (m). The right computerization of ApEn demands 60 values at least (N). Higher ApEn means wide disorder. In that case the values of time-series differs one from each other too much. On the contrary, lower ApEn means regularity. Whatever we want to compare two or more values of ApEn, all ApEn calculations must use the same r, m and N. N is the total number of values of the time-series.. For that reason the ApEn values are symbolized as ApEn(m, r, N). The sensitivity of ApEn algorithm is specified by the researcher himself by choosing the parameters N, m and r. For N < 500 (the most usual in medical papers) we use the “window” m=1. The filter r is always equal to 0,2 * SD. SD is the standard deviation of the time-series.
Access scheme: Publicly accessible
License: An error occurred on the license name.
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