TY - JOUR
T1 - Sample entropy applied to the analysis of synthetic time series and tachograms
AU - Muñoz-Diosdado, A.
AU - Gálvez-Coyt, G. G.
AU - Solís-Montufar, E.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2017/3/8
Y1 - 2017/3/8
N2 - Entropy is a method of non-linear analysis that allows an estimate of the irregularity of a system, however, there are different types of computational entropy that were considered and tested in order to obtain one that would give an index of signals complexity taking into account the data number of the analysed time series, the computational resources demanded by the method, and the accuracy of the calculation. An algorithm for the generation of fractal time-series with a certain value of β was used for the characterization of the different entropy algorithms. We obtained a significant variation for most of the algorithms in terms of the series size, which could result counterproductive for the study of real signals of different lengths. The chosen method was sample entropy, which shows great independence of the series size. With this method, time series of heart interbeat intervals or tachograms of healthy subjects and patients with congestive heart failure were analysed. The calculation of sample entropy was carried out for 24-hour tachograms and time subseries of 6-hours for sleepiness and wakefulness. The comparison between the two populations shows a significant difference that is accentuated when the patient is sleeping.
AB - Entropy is a method of non-linear analysis that allows an estimate of the irregularity of a system, however, there are different types of computational entropy that were considered and tested in order to obtain one that would give an index of signals complexity taking into account the data number of the analysed time series, the computational resources demanded by the method, and the accuracy of the calculation. An algorithm for the generation of fractal time-series with a certain value of β was used for the characterization of the different entropy algorithms. We obtained a significant variation for most of the algorithms in terms of the series size, which could result counterproductive for the study of real signals of different lengths. The chosen method was sample entropy, which shows great independence of the series size. With this method, time series of heart interbeat intervals or tachograms of healthy subjects and patients with congestive heart failure were analysed. The calculation of sample entropy was carried out for 24-hour tachograms and time subseries of 6-hours for sleepiness and wakefulness. The comparison between the two populations shows a significant difference that is accentuated when the patient is sleeping.
UR - http://www.scopus.com/inward/record.url?scp=85016254157&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/792/1/012062
DO - 10.1088/1742-6596/792/1/012062
M3 - Artículo de la conferencia
AN - SCOPUS:85016254157
SN - 1742-6588
VL - 792
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012062
T2 - 8th International Congress of Engineering Physics
Y2 - 7 November 2016 through 11 November 2016
ER -