Abstract
The nonlinear analysis of biological time series provides new possibilities to improve computer aided diagnostic systems, traditionally based on linear techniques. The cardiotocography (CTG) examination records simultaneously the fetal heart rate (FHR) and the maternal uterine contractions. This paper shows, at first, that both signals present nonlinear components based on the surrogate data analysis technique and exploratory data analysis with the return (lag) plot. After that, a nonlinear complexity analysis is proposed considering two databases, intrapartum (CTG-I) and antepartum (CTG-A) with previously identified normal and suspicious/pathological groups. Approximate Entropy (ApEn) and Sample Entropy (SampEn), which are signal complexity measures, are calculated. The results show that low entropy values are found when the whole examination is considered, \(\hbox {ApEn}=0.3244\pm 0.1078\) and \(\hbox {SampEn}=0.2351\pm 0.0758\) (\(\hbox {average}\pm \hbox {standard}\) deviation). Besides, no significant difference was found between the normal (\(\hbox {ApEn}=0.3366\pm 0.1250\) and \(\hbox {SampEn}=0.2532\pm 0.0818\)) and suspicious/pathological (\(\hbox {ApEn}=0.3420\pm 0.1220\) and \(\hbox {SampEn}=0.2457\pm 0.0850\)) groups for the CTG-A database. For a better analysis, this work proposes a windowed entropy calculation considering 5-min window. The windowed entropies presented higher average values (\(\hbox {ApEn}=0.6505\pm 0.2301\) and \(\hbox {SampEn}=0.5290\pm 0.1188\)) for the CTG-A and (\(\hbox {ApEn}=0.5611\pm 0.1970\) and \(\hbox {SampEn}=0.4909\pm 0.1782\)) for the CTG-I. The changes during specific long-term events show that entropy can be considered as a first-level indicator for strong FHR decelerations (\(\hbox {ApEn}=0.1487\pm 0.0341\) and \(\hbox {SampEn}=0.1289\pm 0.0301\)), FHR accelerations (\(\hbox {ApEn}=0.1830\pm 0.1078\) and \(\hbox {SampEn}=0.1501\pm 0.0703\)) and also for pathological behavior such as sinusoidal FHR (\(\hbox {ApEn}=0.1808\pm 0.0445\) and \(\hbox {SampEn}=0.1621\pm 0.0381\)).
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References
Savi MA (2006) Nonlinear Dynamics and Chaos. E-papers
Skinner JE, Molnar M, Vybiral T, Mitra M (1992) Application of chaos theory to biology and medicine. Integr Physiol Behav Sci 27:39–53
Albuquerque VHC, Nunes TM, Pereira DR, Luz EJS, Menotti D, Papa JP, Tavares JMRS (2018) Robust automated cardiac arrythmia detection in ECG beat signals. Neural Comput Appl 29:679–693
Hussein AF, Kumar A, Burbano-Fernandez M, Ramirez-Gonzalez G, Abdulhay E, de Albuquerque VHC (2018) An automated remote cloud-based heart rate variability monitoring system. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2831209
Luz EJS, Nunes TM, Albuquerque VHC, Papa JP, Menotti D (2013) ECG arrhythmia classification based on optimum-path forest. Expert Syst Appl 40:3561–3573
Signorini MG, Sassi R, Lombardi F, Cerutti S (1998) Regularity patterns in heart rate variability signal: the approximate entropy approach. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 20, pp 306–309
Li X, Zheng D, Zhou S, Tang D, Wang C, Wu G (2005) Approximate entropy of fetal heart rate variability as predictor of fetal distress in women at term pregnancy. Acta Obstetricia et Gynecologica Scandinavica 84:837–843
Valle V Jr (2000) Chaos, Complexity and Deterrence. National War College, Abril, core course 5605
Seely AJE, Macklem PT (2004) Complex systems and the technology of variability analysis. Crit Care 8:R367–R384
Doganaksoy A, Göloglu F (2009) On Lempel–Ziv complexity of sequences. Middle East Technical University, Department of Mathematics, Ankara
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88:2297–2301
Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278:H2039–H2049
Ferrario M, Signorini MG, Magenes G, Cerutti S (2006) Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress. IEEE Trans Biomed Eng 53:119–125
Gonçalvez H, Amorim-Costa C, Ayres-de Campos D, Bernardes J (2018) Evolution of linear and nonlinear fetal heart rate indices throughout pregnancy in appropriate, small for gestational age and preterm fetuses: a cohort study. Comput Methods Programs Med 153:191–199
Spyridou K, Chouvarda I, Hadjilentiadis L, Maglaveras N (2017) Linear and nonlinear features of fetal heart rate on the assessment of fetal development in the course of pregnancy and the impact of fetal gender. Physiol Meas 39:1–26
Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge
Kaplan D, Glass L (1995) Understanding nonlinear dynamics. Springer, Berlin
Hornero R, Aboy M, Abasolo D, McNames J, Goldstein B (2005) Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng 52:1671–1680
Pincus S, Singer BH (1995) Randomness and degrees of irregularity. Proc Natl Acad Sci 93:2083–2088
Lu S, Chen X, Kanters JK, Solomon IC, Chon KH (2008) Automatic selection of the threshold value r for approximate entropy. IEEE Trans Biomed Eng 55:1966–1972
Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol 283:R789–R797
Ingemarsson I, Ingemarsson E, Spencer JAD (1993) Fetal heart rate monitoring—a practical guide. Oxford Medical Publications, Oxford University Press, Oxford
Pincus SM, Viscarello RR (1992) Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet Gynecol 79:249–255
Cyzars D, Van Leeuwen P, Bettermann H (2000) Irregularities and nonlinearities in fetal heart period time series in the course of pregnancy. Herzschr Elektrophys 11:179–183
Cao H, Lake DE, Ferguson JE, Chisholm CA, Griffin MP, Moorman JR (2006) Toward quantitative fetal heart rate monitoring. IEEE Trans Biomed Eng 53:111–118
Acknowledgements
The first author thanks to Trium Analysis Online GmBH, MEAC-UFC, LESC-UFC and the Bioengineering Group of the University of Leicester. The third author thanks to CNPQ via Grant No. 426002/2016-4. The fourth author thanks to CNPQ via Grant No. 304315/2017-6.
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Marques, J.A.L., Cortez, P.C., Madeiro, J.P.V. et al. Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. J Supercomput 76, 1305–1320 (2020). https://doi.org/10.1007/s11227-018-2570-8
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DOI: https://doi.org/10.1007/s11227-018-2570-8