Cross Recurrence Quantification Analysis Research Articles

Concept drift detection on social network data using cross-recurrence quantification analysis.

This paper presents our efforts to detect Concept Drifts (changes in data generation processes), using the Cross-Recurrence Quantification Analysis, on time series produced by social network systems. Experiments were performed on the TSViz project (http://www.tsviz.com.br), which collects online tweets associated with predefined hashtags and processes them to generate different time series: one to measure the amount of information contained in textual short messages and another to quantify the positiveness and negativeness of users' sentiments, etc. In that context, this work proposed and evaluated a Concept Drift approach to point out when generating processes change along time, indicating the detection of relevant textual changes in terms of the amount of information and sentiments. As a main contribution, results show that our approach indicates when the most important social events happen, which were confirmed by official news.

Synchronization of mother-infant feeding behavior

In the weaning period, infants are introduced to solid food after being fed solely on milk, which involves a deliberate reorganization of the infant-caregiver feeding interaction. This multiple case study, involving 5 dyads with 10 repeated observations, analyzed its dynamical structure using Cross-Recurrence Quantification Analysis. The results showed that an optimal interaction occurs when the caregiver is leading by roughly 1–2 seconds. During the weaning period, all dyads showed signs of increased synchronization, although there are interesting differences between dyads. These findings indicate that infant-caregiver dyads co-regulate their behavior within a relatively short period.

Cross-Recurrence Quantification Analysis for Inter-Muscular Coordination during Power Grip at Different Force Levels.

This study aimed to examine the dynamical coordination across muscles during power grip at different force levels. Twenty-four healthy volunteers (12 males, 12 females) participated in this experiment. Subjects were instructed to grip a hydraulic hand dynamometer and produce forces at 30%, 50%, 70% Maximal Voluntary Contractions (MVC) for at least 10s, respectively. Surface electromyography (sEMG) signals were simultaneously recorded from eight muscles, including brachioradialis (BR), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), abductor pollicis brevis (APB), first dorsal interosseous (FDI) and abductor digiti minimi (ADM). A cross-recurrence quantification analysis (CRQA) was applied to analyze the sEMG signals by both visualization and quantifications. Results showed that percentage of determinism (%DET) and percentage of entropy (%ENT) of the extrinsic muscle pairs were augmented with increased force levels and had a weak but positive correlation. For intrinsic muscle pairs, the %DET and %ENT increased with force levels but the difference is not or less statistically significant. These results showed that the intermuscular coordination would be alter with force output increased. For the right-handers, the values of intrinsic muscles couplings in right hand were lower than left hand, because less coupled intrinsic muscles contribute to finger dexterity; the reason why the values of extrinsic muscles couplings in right hand were greater than left hand was stronger couplings of extrinsic muscles produced higher synergistic force.

Dynamical Coordination of Hand Intrinsic Muscles for Precision Grip in Diabetes Mellitus

This study investigated the effects of diabetes mellitus (DM) on dynamical coordination of hand intrinsic muscles during precision grip. Precision grip was tested using a custom designed apparatus with stable and unstable loads, during which the surface electromyographic (sEMG) signals of the abductor pollicis brevis (APB) and first dorsal interosseous (FDI) were recorded simultaneously. Recurrence quantification analysis (RQA) was applied to quantify the dynamical structure of sEMG signals of the APB and FDI; and cross recurrence quantification analysis (CRQA) was used to assess the intermuscular coupling between the two intrinsic muscles. This study revealed that the DM altered the dynamical structure of muscle activation for the FDI and the dynamical intermuscular coordination between the APB and FDI during precision grip. A reinforced feedforward mechanism that compensates the loss of sensory feedbacks in DM may be responsible for the stronger intermuscular coupling between the APB and FDI muscles. Sensory deficits in DM remarkably decreased the capacity of online motor adjustment based on sensory feedback, rendering a lower adaptability to the uncertainty of environment. This study shed light on inherent dynamical properties underlying the intrinsic muscle activation and intermuscular coordination for precision grip and the effects of DM on hand sensorimotor function.

Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis

When dealing with semi-supervised scenarios, the Positive and Unlabeled (PU) problem is a special case in which few labeled examples from a single class of interest are received to proceed with the classification of unseen instances, according to their similarities with the known class. In the scope of time series, most of the current studies propose to address this subject using a self-training approach based on the 1-Nearest Neighbor algorithm. In order to compute the most similar instance, they compare features along the time domain using the Euclidean Distance and the Dynamic Time Warping-Delta. Despite time-domain measurements permit the analysis of local series shapes, they disconsider temporal recurrences commonly found in natural phenomena (e.g. population growth, climate studies) and are more sensitive to local noise and fluctuations, leading to poor classification performances as confirmed in this paper. This drawback motivated us to propose the use of the Maximum Diagonal Line of the Cross-Recurrence Quantification Analysis (MDL-CRQA), applied on the time series phase space, as similarity measurement. The phase space is obtained after applying Takens embedding theorem on the series, unfolding temporal relationships and dependencies among data observations. As consequence, by comparing phase spaces rather than the series themselves, we can assess how their trajectories evolve along time, including their periodicities and temporal cycles, as well as decreasing noise influences. Experimental results confirm MDL-CRQA improves classification results for PU time series when compared against the mostly used time-domain similarity measurements.

Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients

Ventricular Tachycardia (VT) is one of the leading causes of sudden cardiac death in the world. Prediction of VT is usually diagnosed by using Electrocardiogram (ECG) and requires expeditious treatment which reduces the mortality rate. The cross recurrence plot (CRP) toolbox is used for computing the recurrence rate values for both (healthy and unhealthy) subjects and artificial neural network (ANN) toolbox in Matlab is used for generating the accurate results. Radial basis function neural network (RBFNN) is used for designing the probabilistic neural network classifier for discriminating the normal from abnormal (VT) signals based on the recurrence rate values. This paper illustrates the cross recurrence quantification analysis (CRQA) of ECG signals followed by the decomposition method using discrete wavelet transform (DWT) for the analysis of cardiac disorders with sensitivity, specificity of 98.5% and 97.6% respectively and overall accuracy achieved is 98.7%. This paper is useful in adopting automated approach for detecting the cardiac arrhythmias efficiently.

Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients

Ventricular Tachycardia (VT) is one of the leading causes of sudden cardiac death in the world. Prediction of VT is usually diagnosed by using Electrocardiogram (ECG) and requires expeditious treatment which reduces the mortality rate. The cross recurrence plot (CRP) toolbox is used for computing the recurrence rate values for both (healthy and unhealthy) subjects and artificial neural network (ANN) toolbox in Matlab is used for generating the accurate results. Radial basis function neural network (RBFNN) is used for designing the probabilistic neural network classifier for discriminating the normal from abnormal (VT) signals based on the recurrence rate values. This paper illustrates the cross recurrence quantification analysis (CRQA) of ECG signals followed by the decomposition method using discrete wavelet transform (DWT) for the analysis of cardiac disorders with sensitivity, specificity of 98.5% and 97.6% respectively and overall accuracy achieved is 98.7%. This paper is useful in adopting automated approach for detecting the cardiac arrhythmias efficiently.

Combining Recurrence Analysis and Automatic Movement Extraction from Video Recordings to Study Behavioral Coupling in Face-to-Face Parent-Child Interactions.

The analysis of parent-child interactions is crucial for the understanding of early human development. Manual coding of interactions is a time-consuming task, which is a limitation in many projects. This becomes especially demanding if a frame-by-frame categorization of movement needs to be achieved. To overcome this, we present a computational approach for studying movement coupling in natural settings, which is a combination of a state-of-the-art automatic tracker, Tracking-Learning-Detection (TLD), and nonlinear time-series analysis, Cross-Recurrence Quantification Analysis (CRQA). We investigated the use of TLD to extract and automatically classify movement of each partner from 21 video recordings of interactions, where 5.5-month-old infants and mothers engaged in free play in laboratory settings. As a proof of concept, we focused on those face-to-face episodes, where the mother animated an object in front of the infant, in order to measure the coordination between the infants' head movement and the mothers' hand movement. We also tested the feasibility of using such movement data to study behavioral coupling between partners with CRQA. We demonstrate that movement can be extracted automatically from standard definition video recordings and used in subsequent CRQA to quantify the coupling between movement of the parent and the infant. Finally, we assess the quality of this coupling using an extension of CRQA called anisotropic CRQA and show asymmetric dynamics between the movement of the parent and the infant. When combined these methods allow automatic coding and classification of behaviors, which results in a more efficient manner of analyzing movements than manual coding.

Multiscale synchrony behaviors of paired financial time series by 3D multi-continuum percolation

Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empirical mode decomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison.

Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development.

Environmental exposures to essential and toxic elements may alter health trajectories, depending on the timing, intensity, and mixture of exposures. In epidemiologic studies, these factors are typically analyzed as a function of elemental concentrations in biological matrices measured at one or more points in time. Such an approach, however, fails to account for the temporal cyclicity in the metabolism of environmental chemicals, which if perturbed may lead to adverse health outcomes. Here, we conceptualize and apply a non-linear method–recurrence quantification analysis (RQA)–to quantify cyclical components of prenatal and early postnatal exposure profiles for elements essential to normal development, including Zn, Mn, Mg, and Ca, and elements associated with deleterious health effects or narrow tolerance ranges, including Pb, As, and Cr. We found robust evidence of cyclical patterns in the metabolic profiles of nutrient elements, which we validated against randomized twin-surrogate time-series, and further found that nutrient dynamical properties differ from those of Cr, As, and Pb. Furthermore, we extended this approach to provide a novel method of quantifying dynamic interactions between two environmental exposures. To achieve this, we used cross-recurrence quantification analysis (CRQA), and found that elemental nutrient-nutrient interactions differed from those involving toxicants. These rhythmic regulatory interactions, which we characterize in two geographically distinct cohorts, have not previously been uncovered using traditional regression-based approaches, and may provide a critical unit of analysis for environmental and dietary exposures in epidemiological studies.

Factorization of Force and Timing in Sensorimotor Performance: Long-Range Correlation Properties of Two Different Task Goals.

Long-range correlations are often manifested in the form of 1/fβ noise in a series of repeated measurements of the same neural or behavioral variable. Recent work has demonstrated that the magnitude and nature of these long-range correlations reliably capture individual differences and variation in task performance. In sensorimotor timing experiments, task characteristics such as tapping or circle drawing affect these long-range correlations during the production of isochronous time intervals. Such correlations are highly reproducible across multiple trials for the same task but do not correlate between tasks. In the present experiment, we investigate whether two behavioral variables that are simultaneously controlled by the same participant in a given experimental condition can show such differentially organized fluctuations. In order to answer this question, 13 participants were asked to produce repetitive movements with their right index finger at a specified time interval (500ms) and a specified force (8N) in the absence of an auditory metronome and visual feedback of force levels following a synchronization-continuation paradigm. Although participants showed high levels of consistency in the long-range correlations for each task component separately over multiple trials/observations, the long-range fluctuations for force and timing were found to show no correlations with each other for each participant. Cross recurrence quantification analyses (CRQA) revealed that there was limited shared structure between the timing and force time series data. Taken together, these results suggest that complex systems can organize multiple processes in a relatively independent manner while maintaining a high degree of reliability within one task parameter.

Recurrence Quantification for the Analysis of Coupled Processes in Aging.

Aging is a complex phenomenon, with numerous simultaneous processes that interact with each other on a moment-to-moment basis. One way to quantify the interactions of these processes is by measuring how much a process is similar to its own past states or the past states of another system through the analysis of recurrence. This paper presents an introduction to recurrence quantification analysis (RQA) and cross-recurrence quantification analysis (CRQA), two dynamical systems analysis techniques that provide ways to characterize the self-similar nature of each process and the properties of their mutual temporal co-occurrence. We present RQA and CRQA and demonstrate their effectiveness with an example of conversational movements across age groups. RQA and CRQA provide methods of analyzing the repetitive processes that occur in day-to-day life, describing how different processes co-occur, synchronize, or predict each other and comparing the characteristics of those processes between groups. With intensive longitudinal data becoming increasingly available, it is possible to examine how the processes of aging unfold. RQA and CRQA provide information about how one process may show patterns of internal repetition or echo the patterning of another process and how those characteristics may change across the process of aging.

Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis

The present study is aimed at the classification of mild cognitive impairment (MCI) EEG by combining complexity and synchronization features based on quantifiers from the common platform of recurrence based analysis. Recurrence rate (RR) of recurrence quantification analysis (RQA) is used for complexity analysis and RR of cross recurrence quantification analysis (CRQA) is used for synchronization analysis. The investigations are carried out on EEG from two states (i) resting eyes closed (EC) and (ii) short term memory task (STM).The results of our analysis show lower levels of complexity and higher levels of inter and intra hemispheric synchronisation in the MCI EEG compared to that of normal controls (NC) as indicated by the statistically significant higher value of RQA RR and CRQA RR. The results also evidence the effectiveness of memory activation task by bringing out the characteristic features of MCI EEG in task specific regions of temporal, parietal and frontal lobes under the STM condition.A new approach of combining complexity and synchronization features for EEG classification of MCI subjects is proposed, based on the geometrical signal separation in a feature space formed by RQA and CRQA RR values. The results of linear classification analysis of MCI and NC EEG also reveals the effectiveness of task state analysis by the enhanced classification efficiency under the cognitive load of STM condition compared to that of EC condition.

Quantifying and Modeling Coordination and Coherence in Pedestrian Groups.

Coherent collective behavior emerges from local interactions between individuals that generate group dynamics. An outstanding question is how to quantify group coordination of non-rhythmic behavior, in order to understand the nature of these dynamics at both a local and global level. We investigate this problem in the context of a small group of four pedestrians walking to a goal, treating their speed, and heading as behavioral variables. To measure the local coordination between pairs of pedestrians, we employ cross-correlation to estimate coupling strength and cross-recurrence quantification (CRQ) analysis to estimate dynamic stability. When compared to reshuffled virtual control groups, the results indicate lower-dimensional behavior and a stronger, more stable coupling of walking speed in real groups. There were no differences in heading alignment observed between the real and virtual groups, due to the common goal. By modeling the local speed coupling, we can simulate coordination at the dyad and group levels. The findings demonstrate spontaneous coordination in pedestrian groups that gives rise to coherent global behavior. They also offer a methodological approach for investigating group dynamics in more complex settings.

Conversing with a devil's advocate: Interpersonal coordination in deception and disagreement.

This study investigates the presence of dynamical patterns of interpersonal coordination in extended deceptive conversations across multimodal channels of behavior. Using a novel "devil’s advocate" paradigm, we experimentally elicited deception and truth across topics in which conversational partners either agreed or disagreed, and where one partner was surreptitiously asked to argue an opinion opposite of what he or she really believed. We focus on interpersonal coordination as an emergent behavioral signal that captures interdependencies between conversational partners, both as the coupling of head movements over the span of milliseconds, measured via a windowed lagged cross correlation (WLCC) technique, and more global temporal dependencies across speech rate, using cross recurrence quantification analysis (CRQA). Moreover, we considered how interpersonal coordination might be shaped by strategic, adaptive conversational goals associated with deception. We found that deceptive conversations displayed more structured speech rate and higher head movement coordination, the latter with a peak in deceptive disagreement conversations. Together the results allow us to posit an adaptive account, whereby interpersonal coordination is not beholden to any single functional explanation, but can strategically adapt to diverse conversational demands.

Recurrence plot-based dynamic analysis on electrochemical noise of the evolutive corrosion process

The cross recurrence plot (CRP) method was applied on the simultaneous current and voltage fluctuation time records in consideration of their possible nonlinear kinetics. Sequential cross recurrence quantification analysis (CRQA), CRP pattern recognition and synchronization analysis were used to screen the paralleled running from the uncorrelated oscillations, to recognize the impedance component, and to identify the synchronization type and degree in the dynamical view point. With the aid of the proposed approach, the sulfur-induced localized corrosion of the alloy 718 in the high temperature brine was detected and evaluated during the evolutive corrosion process.

The Stance Leads the Dance: The Emergence of Role in a Joint Supra-Postural Task.

Successfully meeting a shared goal usually requires co-actors to adopt complementary roles. However, in many cases, who adopts what role is not explicitly predetermined, but instead emerges as a consequence of the differences in the individual abilities and constraints imposed upon each actor. Perhaps the most basic of roles are leader and follower. Here, we investigated the emergence of “leader-follower” dynamics in inter-personal coordination using a joint supra-postural task paradigm (Ramenzoni et al., 2011; Athreya et al., 2014). Pairs of actors were tasked with holding two objects in alignment (each actor manually controlled one of the objects) as they faced different demands for stance (stable vs. difficult) and control (which actor controlled the larger or smaller object). Our results indicate that when actors were in identical stances, neither led the inter-personal (between actors) coordination by any systematic fashion. Alternatively, when asymmetries in postural demands were introduced, the actor with the more difficult stance led the coordination (as determined using cross-recurrence quantification analysis). Moreover, changes in individual stance difficulty resulted in similar changes in the structure of both intra-personal (individual) and inter-personal (dyadic) coordination, suggesting a scale invariance of the task dynamics. Implications for the study of interpersonal coordination are discussed.

Investigation of hydrodynamics of gas-solid fluidized beds using cross recurrence quantification analysis

Hydrodynamics of gas-solid fluidized bed was investigated by analyzing its pressure fluctuations using cross recurrence quantification analysis (CRQA). Pressure fluctuations were measured in a lab scale fluidized bed of various particle sizes at different gas velocities. First, the CRQA was applied to a number of well-known dynamic systems and the results demonstrated that it is a powerful method to detect similarities between nonlinear signals. Then, it was shown that graphical structures within the cross recurrence plot of pressure fluctuations of a fluidized bed vary with both superficial gas velocity and particle size. It was found that determinism and cross recurrence rate of non-normalized data initially decrease and then increase with increasing the gas velocity. When the signal is initially normalized, determinism and entropy do not change with the superficial gas velocity while cross recurrence rate is sensible to changes in the superficial gas velocity. It was concluded that entropy can be used for detecting changes of particle size and if a proper reference state is chosen, entropy can be a powerful index for detecting changes in the size of particles in a fluidized bed.

Online Detection of Hydrodynamic Changes in Fluidized Bed using Cross Average Diagonal Line

Online detection of hydrodynamics of gas-solid fluidized bed was characterized using pressure fluctuations by cross recurrence plot (CRP) and cross recurrence quantification analysis (CRQA). Experiments were conducted in a lab scale fluidized bed of various particle sizes 150 μm, 280 μm and 490 μm at different gas velocities. Firstly, pattern changes of cross recurrence plot were discussed and then reference states was selected. Afterwards, cross average diagonal line (CLave) of other states corresponding to reference states were obtained. It was found that cross average diagonal line of non-normalized data initially decreases and then increases with increasing the gas velocity. When the signal is initially normalized, cross average diagonal line does not change with the superficial gas velocity. It was concluded that cross average diagonal line could be used for detecting small changes of particle size and if a proper reference state is chosen, it can be perceived as a powerful index for detecting changes in the size of particles in a fluidized bed.

Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis.

The aim of this investigation was to compare and contrast the use of cross sample entropy (xSE) and cross recurrence quantification analysis (cRQA) measures for the assessment of coupling of rhythmical patterns. Measures were assessed using simulated signals with regular, chaotic, and random fluctuations in frequency, amplitude, and a combination of both. Biological data were studied as models of normal and abnormal locomotor-respiratory coupling. Nine signal types were generated for seven frequency ratios. Fifteen patients with COPD (abnormal coupling) and twenty-one healthy controls (normal coupling) walked on a treadmill at three speeds while breathing and walking were recorded. xSE and the cRQA measures of percent determinism, maximum line, mean line, and entropy were quantified for both the simulated and experimental data. In the simulated data, xSE, percent determinism, and entropy were influenced by the frequency manipulation. The 1 : 1 frequency ratio was different than other frequency ratios for almost all measures and/or manipulations. The patients with COPD used a 2 : 3 ratio more often and xSE, percent determinism, maximum line, mean line, and cRQA entropy were able to discriminate between the groups. Analysis of the effects of walking speed indicated that all measures were able to discriminate between speeds.