Classifying healthy women and preeclamptic patients from cardiovascular data using recurrence and complex network methods

G. M. Ramírez Ávila, A. Gapelyuk, N. Marwan, H. Stepan, J. Kurths, T. Walther, N. Wessel

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

20 Citas (Scopus)


It is urgently aimed in prenatal medicine to identify pregnancies, which develop life-threatening preeclampsia prior to the manifestation of the disease. Here, we use recurrence-based methods to distinguish such pregnancies already in the second trimester, using the following cardiovascular time series: the variability of heart rate and systolic and diastolic blood pressures. We perform recurrence quantification analysis (RQA), in addition to a novel approach, ε-recurrence networks, applied to a phase space constructed by means of these time series. We examine all possible coupling structures in a phase space constructed with the above-mentioned biosignals. Several measures including recurrence rate, determinism, laminarity, trapping time, and longest diagonal and vertical lines for the recurrence quantification analysis and average path length, mean coreness, global clustering coefficient, assortativity, and scale local transitivity dimension for the network measures are considered as parameters for our analysis. With these quantities, we perform a quadratic discriminant analysis that allows us to classify healthy pregnancies and upcoming preeclamptic patients with a sensitivity of 91.7% and a specificity of 45.8% in the case of RQA and 91.7% and 68% when using ε-recurrence networks, respectively.

Idioma originalInglés
Páginas (desde-hasta)103-110
Número de páginas8
PublicaciónAutonomic Neuroscience: Basic and Clinical
EstadoPublicada - nov. 2013

Nota bibliográfica

Funding Information:
This work has been financially supported by the German Academic Exchange Service (DAAD), the Deutsche Forschungsgemeinschaft (grant nos. KU 837/20-1 and KU-837/29-2 ), the Federal Ministry of Economics and Technology (grant no. FKZ KF2248001FR9 ), and the European projects EU NEST-pathfinder and BRACCIA.


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