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Proceedings of the International Symposium on Nonlinear Theory and its Applications (NOLTA2010), Krakow(6170), 585–588 (2010)
Recurrence Based Complex Network Analysis of Cardiovascular Variability Data to Predict Pre-Eclampsia
N. Marwan, N. Wessel, H. Stepan, J. KurthsWe propose a novel approach for analysing time series using complex network theory. We identify the recurrence matrix (calculated from time series) with the adjacency matrix of a complex network and apply measures for the characterisation of complex networks to this recurrence matrix. We illustrate similarities and differences between the recurrence quantification analysis and the complex network analysis. By using the logistic map, we demonstrate the potential of the complex network measures for the detection of different dynamical regimes. Pre-eclampsia in pregnancy is a serious disease with high risk of fetal and maternal morbidity. The usual positive predictive value is 20–30%. Including heart rate variability, it has been recently shown that this predictive power can be improved. In order to predict pre-eclampsia, we are analysing time series of systolic and diastolic blood pressure as well as heart rate variability measured in the 20th week of gestation.. We demonstrate the potential of the complex network measures for a further improvement of the positive predictive value of pre-eclampsia.
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