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Open AccessArticle

Entropy and Compression Capture Different Complexity Features: The Case of Fetal Heart Rate

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Department of Community Medicine, Information and Health Decision Sciences—MEDCIDS, Faculty of Medicine, University of Porto, Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal
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Center for Health Technology and Services Research—CINTESIS, Faculty of Medicine, University of Porto, Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal
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Department of Obstetrics and Gynecology, Faculty of Medicine of University of Porto, Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal
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Pedro Hispano Hospital, Local Health Unit of Matosinhos, 4464-513 Matosinhos, Portugal
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Computer Science Department, Faculty of Science, University of Porto, Rua do Campo Alegre 1021/1055, 4169-007 Porto, Portugal
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CRACS/INESC-TEC, University of Porto, Rua Dr. Roberto Frias, 4200 Porto, Portugal
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(12), 688; https://doi.org/10.3390/e19120688
Received: 21 September 2017 / Revised: 28 November 2017 / Accepted: 11 December 2017 / Published: 14 December 2017
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study aims at characterizing the complexity of FHR features captured by entropy and compression, using as reference international guidelines. Single and multi-scale approaches were considered in the computation of entropy and compression. The following physiologic-based features were considered: FHR baseline; percentage of abnormal long (%abLTV) and short (%abSTV) term variability; average short term variability; and, number of acceleration and decelerations. All of the features were computed on a set of 68 intrapartum FHR tracings, divided as normal, mildly, and moderately-severely acidemic born fetuses. The correlation between entropy/compression features and the physiologic-based features was assessed. There were correlations between compressions and accelerations and decelerations, but neither accelerations nor decelerations were significantly correlated with entropies. The %abSTV was significantly correlated with entropies (ranging between −0.54 and −0.62), and to a higher extent with compression (ranging between −0.80 and −0.94). Distinction between groups was clearer in the lower scales using entropy and in the higher scales using compression. Entropy and compression are complementary complexity measures. View Full-Text
Keywords: fetal heart rate; entropy; data compression; complexity analysis; nonlinear analysis fetal heart rate; entropy; data compression; complexity analysis; nonlinear analysis
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Monteiro-Santos, J.; Gonçalves, H.; Bernardes, J.; Antunes, L.; Nozari, M.; Costa-Santos, C. Entropy and Compression Capture Different Complexity Features: The Case of Fetal Heart Rate. Entropy 2017, 19, 688.

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