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

Ramírez-Ávila, Gonzalo Marcelo; Gapelyuk, Andrej; Marwan, Norbert; Stepan, Holger; Kurths, Jüergen; Walther, T; Wessel, Niels

doi:10.1016/j.autneu.2013.05.003

Cited by 20 publications

(5 citation statements)

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“…In the Fixed Distance Method, the threshold is defined depending on the standard deviation of the particular frame, as this is one of the criteria especially suited for signal detection [ 78 ]. The selected values are based on the work done by Ramírez et al [ 51 ]. For the FAN criterion, 12 different values were tested, ranging from 1% to 25%, based partially on the values chosen by Nguyen et al [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, they also suggested that further studies would be necessary to compare results in other physiological conditions, such as illness. In the last 15 years, many authors have introduced RQA, among others, for the analysis of HRV in ventricular tachycardia [ 33 ], ventricular arrhythmias [ 32 ], sleep apnea [ 11 – 13 , 34 ], diabetes mellitus [ 41 ], paroxysmal atrial fibrillation [ 36 ], preeclampsia [ 51 ], and vasovagal syncopes [ 52 ]. So RPs and RQA can be considered a classical tool to analyse the cardiac system.…”

Section: Introductionmentioning

confidence: 99%

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Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

et al. 2018

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Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

et al. 2018

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“…For example, this has been reported in Ref. [33] where a classification of healthy women and preeclamptic patients based on cardiovascular time series has been performed with the aim of performing early prediction of preeclampsia. Another example is found in Ref.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data

et al. 2016

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We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.

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“…Typical values of DET and T for different dynamical regimes in the Rössler system. can help, e.g., to detect serious diseases, such as preeclampsia29 , to detect epileptic states…”

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confidence: 99%

Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

Marwan

Kurths

2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

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

Cited by 20 publications

References 30 publications

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data

Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

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