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

Study of interaction and complete merging of binary cyclones using complex networks

Cyclones are among the most hazardous extreme weather events on Earth. In certain scenarios, two co-rotating cyclones in close proximity to one another can drift closer and completely merge into a single cyclonic system. Identifying the dynamic transitions during such an interaction period of binary cyclones and predicting the complete merger (CM) event are challenging for weather forecasters. In this work, we suggest an innovative approach to understand the evolving vortical interactions between the cyclones during two such CM events (Noru-Kulap and Seroja-Odette) using time-evolving induced velocity-based unweighted directed networks. We find that network-based indicators, namely, in-degree and out-degree, quantify the changes in the interaction between the two cyclones and are excellent candidates to classify the interaction stages before a CM. The network indicators also help to identify the dominant cyclone during the period of interaction and quantify the variation of the strength of the dominating and merged cyclones. Finally, we show that the network measures also provide an early indication of the CM event well before its occurrence.

Multiplex Recurrence Network Analysis of Inter-Muscular Coordination During Sustained Grip and Pinch Contractions at Different Force Levels

Production of functional forces by human motor systems require coordination across multiple muscles. Grip and pinch are two prototypes for grasping force production. Each grasp plays a role in a range of hand functions and can provide an excellent paradigm for studying fine motor control. Despite previous investigations that have characterized muscle synergies during general force production, it is still unclear how intermuscular coordination differs between grip and pinch and across different force outputs. Traditional muscle synergy analyses, such as non-negative matrix factorization or principal component analysis, utilize dimensional reduction without consideration of nonlinear characteristics of muscle co-activations. In this study, we investigated the novel method of multiplex recurrence networks (MRN) to assess the inter-muscular coordination for both grip and pinch at different force levels. Unlike traditional methods, the MRN can leverage intrinsic similarities in muscle contraction dynamics and project its layers to the corresponding weighted network (WN) to better model muscle interactions. Twenty-four healthy volunteers were instructed to grip and pinch an apparatus with force production at 30%, 50%, and 70% of their respective maximal voluntary contraction (MVC). The surface electromyography (sEMG) signals were recorded from eight muscles, including intrinsic and extrinsic muscles spanning the hand and forearm. The sEMG signals were then analyzed using MRNs and WNs. Interlayer mutual information ( I ) and average edge overlap ( ω ) of MRNs and average shortest path length ( L ) of WNs were computed and compared across groups for grasp types (grip vs. pinch) and force levels (30%, 50% and 70% MVC). Results showed that the extrinsic, rather than the intrinsic muscles, had significant differences in network parameters between both grasp types ( ), and force levels ( ), and especially at higher force levels. Furthermore, I and ω were strengthened over time ( ) except with pinch at 30% MVC. Results suggest that the central nervous system (CNS) actively increases cortical oscillations over time in response to increasing force levels and changes in force production with different sustained grasping types. Muscle coupling in extrinsic muscles was higher than in intrinsic muscles for both grip and pinch. The MRNs may be a valuable tool to provide greater insights into inter-muscular coordination patterns of clinical populations, assess neuromuscular function, or stabilize force control in prosthetic hands.

Sequential analysis of heart rate variability, blood pressure variability and baroreflex sensitivity in healthy pregnancy

OBJECTIVE

The aim of this study was to demonstrate the temporal profile of changes in heart rate variability (HRV), blood pressure variability (BPV), and cardiac baroreflex sensitivity (BRS) during the course of a healthy pregnancy.

MATERIALS AND METHODS

This was a longitudinal study during which autonomic variability parameters (HRV, BPV, BRS) were assessed in 66 pregnant women at 11-13, 20-22 and 30-32 weeks of gestation. A lead II electrocardiogram tracing and beat-to-beat blood pressure were recorded with the subject breathing spontaneously in the supine position. Changes in the parameters were analyzed using repeated measures analysis of variance.

RESULTS

Overall HRV (SDNN; standard deviation of all NN intervals) was found to decrease significantly over the course of pregnancy (p < 0.05). Similarly, indices which represent the parasympathetic component of these variables (SDSD [standard deviation of differences between adjacent NN intervals]; pNN50 [NN50 count {number of pairs of adjacent NN intervals differing by more than 50 ms} divided by the total number of all NN intervals]; high-frequency [HF] power) were also found to decrease significantly from the first to third trimester of pregnancy (p < 0.05). Low-frequency (LF) power increased over the course of pregnancy (p < 0.05). The LF/HF ratio increased significantly from first to third trimester of pregnancy (median: 0.66 [first trimester] vs.1.02 [second] vs. 0.91 [third]; p < 0.05) Overall BPV increased during the course of pregnancy, with a significant rise in the HF component of BPV and a significant fall in the LF component of BPV with advancing gestation (p < 0.05). BRS decreased over the course of pregnancy (median: 16.31, interquartile range [IQR] 11.04-23.13 [first trimester] vs. 11.42, IQR 8.54-19.52 [second] vs. 8.84, IQR 7.15-12.45 [third] ms/mmHg; p < 0.05).

CONCLUSION

Pregnancy is associated with decreased vagal and increased sympathetic modulation of cardiac autonomic tone with advancing gestation, together with increased BPV. The reduction in cardiac BRS may play a role in increasing BPV and decreasing HRV over the course of pregnancy.

Ordinal partition transition network based complexity measures for inferring coupling direction and delay from time series

It has been demonstrated that the construction of ordinal partition transition networks (OPTNs) from time series provides a prospective approach to improve our understanding of the underlying dynamical system. In this work, we introduce a suite of OPTN based complexity measures to infer the coupling direction between two dynamical systems from pairs of time series. For several examples of coupled stochastic processes, we demonstrate that our approach is able to successfully identify interaction delays of both unidirectional and bidirectional coupling configurations. Moreover, we show that the causal interaction between two coupled chaotic Hénon maps can be captured by the OPTN based complexity measures for a broad range of coupling strengths before the onset of synchronization. Finally, we apply our method to two real-world observational climate time series, disclosing the interaction delays underlying the temperature records from two distinct stations in Oxford and Vienna. Our results suggest that ordinal partition transition networks can be used as complementary tools for causal inference tasks and provide insights into the potentials and theoretical foundations of time series networks.

Using recurrence plots for the analysis of the nonlinear dynamical response of iron passivation-corrosion processes

Recurrence plots (RPs) and recurrence quantification analysis (RQA) are used in this work to study different nonlinear dynamical regimes emerging in an electrochemical system, namely, the electrodissolution-passivation of iron in chloride-containing sulfuric acid solutions. Current oscillations at different applied potentials and chloride concentrations exhibit bifurcations from periodic to complex (bursting) periodic and aperiodic or chaotic behaviors, associated with different dissolution states of iron. The clarification of these transitions is essential to understand the type of corrosion (uniform or localized) taking place as well as the underlying mechanisms governing the stability of the metal. The RQA reveals that the predictability of the chloride-perturbed Fe|0.75M H₂SO₄ system strongly depends on the chloride concentration and the applied potential. At relatively low chloride concentrations, RQA measures, based on vertical and diagonal structures in RPs, display a decrease upon the breakdown of the passivity on iron and the initiation of localized corrosion (pitting). Phases of pitting corrosion (propagation/growth and unstable pitting) that followed pit initiation are discerned by keen changes of complexity measures upon varying the applied potential. At higher chloride concentrations, the evolution of RQA measures with the potential signifies a transition from the passive-active state dissolution to the polishing state dissolution of iron inside pits. The increase of the applied potential at late stages of pitting corrosion increases the nonlinear correlations and thus the complexity of the system decreases, which corroborates the RQA.

Frequency Network Analysis of Heart Rate Variability for Obstructive Apnea Patient Detection

Obstructive sleep apnea (OSA) is a popular sleep disorder. Traditional OSA diagnosis methods are cumbersome and expensive, which bring inconvenience for patient diagnosis and heavy workload for physician. Automatically identifying OSA patients from electrocardiogram (ECG) records is important for clinical diagnosis and treatment. In this paper, a new method based on the frequency and network domains is proposed to automatically recognize OSA patients with nocturnal ECG records. First, each RR-interval (beat to beat heart rate) series was divided into segments. By calculating the power spectral density (PSD) of heart rate variability segment with Lomb-Scargle method, the dynamic time warping (DTW) distance was used to evaluate the similarity (dissimilarity) of the lower frequency in the PSD series, then the DTW distance matrix was transformed to a binary matrix, and then network metrics were calculated to discriminate OSA patients with healthy subjects. The new method was tested with data of 389 subjects collected from two public databases that consist of normal subjects without OSA (apnea-hypopnea index, AHI 5) and OSA patients (AHI 5). Results show that a single network metric (local clustering coefficient) can recognize OSA patients with 90.1% accuracy, 88.29% sensitivity, and 90.5% specificity, and confirm the potential of using the ECG records for OSA patients recognition.

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

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.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

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

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.