Recurrence Quantification Analysis Method Research Articles

An alternative EMG normalization method: Heterogeneous recurrence quantification analysis of isometric maximum voluntary contraction movements

This study presents an alternative method of normalizing Surface Electromyography (EMG) signals, which provide valuable insights into the musculoskeletal properties of the human body. Traditional normalization through the peak maximum voluntary contraction (MVC) method may result in variable outcomes due to its dependence on pre-processing steps such as smoothing and window length. To address this issue, the study standardizes the EMG pre-processing steps by using non-linear techniques, namely recurrence quantification analysis (RQA) and heterogeneous RQA (HRQA). RQA, specifically HRQA, functions as EMG feature extractors to explore the nonlinear dynamical system and state space trajectories of chaotic EMG signals. Principal component analysis (PCA) and kernel PCA (KPCA) were further applied to the RQA and HRQA results to obtain data with more information. The results show that the first principal components (PC1 and KPC1) of HRQA have a very strong relationship with peak MVC (ρ > |0.90|, p < 0.001), indicating their potential compared to the traditional EMG normalization technique for lower limb muscle groups. Additionally, the study investigated the dynamic differences among isometric MVC movements in lower limb muscle groups and found that the RQA and HRQA methods indicated a greater separation for detecting these differences compared to peak MVC. This study offers a valuable method for assessing the musculoskeletal characteristics of individuals who may have difficulty with activities like walking or providing maximum force during EMG data collection, such as those suffering from Parkinson's disease or walking disabilities. This enhances our understanding of their musculoskeletal properties.

Tactile perception of textile fabrics using event-related potentials and nonlinear methods

Tactile perception is processed in the somatosensory cortices of the brain. To study the electrical activity of the brain evoked by the tactile perceptions of fabric textures, electroencephalograph signals during tactile perception were evaluated using event-related potentials and recurrence quantification analysis methods. A support vector machine was used to realize the identification of electroencephalograph signals. The results showed that fabric surface with large surface roughness, critical buckling force, and fiber diameter, and low warp-weft density was preferentially perceived, need large attentional resources, and evoke strong nonstationary state of the electroencephalographic system during the tactile perception. The δ rhythm was the main rhythm of event-related potential signals evoked by tactile perception of fabrics and the main energy of the electroencephalograph rhythm was concentrated in the low frequency band. The electroencephalographic system was in a strong nonstationary and fluctuating state during the production stage of P200 and P300 components of the event-related potential wave. The combination of P200, P300, mean diagonal line length L, and trapping time TT can significantly improve the classification accuracy of the electroencephalograph signal of tactile perception to about 72%.

Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection

Due to the complexity and non-stationarity of the voice generation system, the nonlinearity of speech signals cannot be accurately quantified. Recently, the recurrence quantification analysis method has been used for voice disorder detection. In this paper, multiscale recurrence quantification measures (MRQMs) are proposed. The signals are reconstructed in the high-dimensional phase space at the equivalent rectangular bandwidth scale. Recurrence plots (RPs) combining the characteristics of human auditory perception are drawn with an appropriate recurrence threshold. Based on the above, the nonlinear dynamic recurrence features of the speech signal are quantized from the recurrence plot of each frequency channel. Furthermore, this paper explores the recurrence quantification thresholds that are most suitable for pathological voices. Our results show that the proposed MRQMs with support vector machine (SVM), random forest (RF), Bayesian network (BN) and Local Weighted Learning (LWL) achieve an average accuracy of 99.45%, outperforming traditional features and other complex measurements. In addition, MRQMs also have the potential for multi-classification of voice disorder, achieving an accuracy of 89.05%. This study demonstrates that MRQMs can characterize the recurrence characteristic of pathological voices and effectively detect voice disorders.

EEG‐based automatic multi‐class classification of epileptic seizure types using recurrence plots

AbstractThere is an urgent need to develop an efficient system for accurate recognition of epileptic seizure type that could play a significant role in reducing the adversial effects of the disease. A lot of work is available for EEG based automatic seizure detection but very less attempts have been made towards the classification of variants of seizures. Moreover, none of the authors have included the EEG signals for myoclonic seizure type in their classification studies. Our study aims to propose the automatic machine learning based multi‐class classification system for classifying six types of epileptic seizures (such as absence, focal non‐specific, complex‐partial, generalized, tonic–clonic and including myoclonic too) through the use of recurrence plots (RPs). In our study, we have collected 19‐channel EEG data from a huge database of Temple University Hospital. Two classification modalities have been proposed depending upon the feature extraction approach followed for RPs‐ (a) Traditional approach using the recurrence quantification analysis (RQA) method; (b) Texture based approach using the hybrid method named as Unthresholded Recurrence Plot with Fractal Weighted Local Binary Pattern (URP‐FWLBP), that has been proposed in our work using a combination of Unthresholded RPs (URPs) and Fractal Weighted Local Binary Pattern (FWLBP) method. Thereby, an indirect variant of support vector machine (one‐vs‐rest approach) has been used as the multi‐class classifier in both the approaches. The performance of the proposed modalities has been validated using five‐fold cross‐validation method in the light of seven metrics‐ accuracy, sensitivity, specificity, Matthews correlation coefficient, geometric‐mean, precision and f1‐score. The experimental results show the effectiveness of the proposed system following hybrid URP‐FWLBP method in performing multi‐class epileptic seizure type classification with 100% efficiency, thereby outperforming the traditional RQA method based system as well the existing state‐of‐the‐art systems.

Recurrence quantification analysis of heart rate variability to detect both ventilatory thresholds.

Aims of this study were: to verify if Recurrence Quantification Analysis (RQA) of Heart Rate Variability (HRV) time series could determine both ventilatory thresholds in individuals with different fitness levels, and to assess the validity of RQA method compared to gas-exchange method (GE). The two thresholds were estimated in thirty young individuals during incremental exercise on cycle-ergometer: Heart rate (HR), Oxygen consumption (VO2) and Workload were measured by the two methods (RQA and GE). Repeated measures ANOVA was used to assess main effects of methods and methods-by-groups interaction effects for HR, VO2 and Workload at aerobic (AerT) and anaerobic (AnT) thresholds. Validity of RQA at both thresholds was assessed for HR, VO2 and Workload by Ordinary Least Products (OLP) regression, Typical Percentage Error (TE), Intraclass Correlation Coefficients (ICC) and the Bland Altman plots. No methods-by-groups interaction effects were detected for HR, VO2 and Workload at AerT and AnT. The OLP analysis showed that at both thresholds RQA and GE methods had very strong correlations (r >0.8) in all variables (HR, VO2 and Workload). Slope and intercept values always included the 1 and the 0, respectively. At AerT the TE ranged from 4.02% (5.48 bpm) to 10.47% (8.53 Watts) (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.85). At AnT the TE ranged from 2.53% (3.98 bpm) to 6.64% (7.81 Watts) (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.90). Therefore, RQA of HRV time series is a new valid approach to determine both ventilatory thresholds in individuals with different physical fitness levels, it can be used when gas analysis is not possible or not convenient.

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package includes implementations of several recent advances in recurrence based analysis, among them applications to multivariate data and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.

Comparing the HRV Time-Series Signals Acquired from Cannabis Consuming and Non-Consuming Indian Paddy-Field Workers by Recurrence Quantification Analysis

ObjectiveIn the last few decades, the consumption of cannabis-based products for recreational purposes has dramatically increased. Unfortunately, cannabis consumption has been associated with the incidences of cardiovascular diseases. Hence, there is a necessity for understanding the plausible mechanics of cardiophysiological changes due to cannabis consumption. Accordingly, the current study was designed to understand the suitability of the recurrence quantification analysis (RQA) method in detecting the changes in the heart rate variability (HRV) time-series signals due to the consumption of cannabis (bhang). Further, a machine learning model has been proposed for the automated detection of the cannabis takers. Materials and MethodsThe RQA of the HRV time-series signals from 200 healthy Indian male paddy-field workers were carried out. The obtained parameters were statistically analyzed using the Mann-Whitney U test. Further, the decision trees, weight-based feature ranking, and dimensionality reduction methods were employed for identifying the relevant features for the development of a suitable machine learning model. ResultsObservable changes in the patterns of the recurrence plots among the bhang consuming and non-consuming groups were noticed. However, there were no significant differences in the RQA parameters. Among the developed machine learning models, the SVM model obtained from the “Information gain ratio” feature selection method exhibited the highest accuracy (%) of 69.09 ± 9.33. ConclusionOur study suggests that the RQA method is not as effective as the time and frequency domain methods for detecting the alterations in the HRV time-series signals due to cannabis consumption. The SVM model was found to be the best model for the automated detection of cannabis takers. The selection of the features by the information gain ratio method played an important role in the development of the optimized SVM model.

Chatter Detection in Milling of Carbon Fiber-Reinforced Composites by Improved Hilbert-Huang Transform and Recurrence Quantification Analysis.

In the paper, the problem of chatter vibration detection in the milling process of carbon fiber-reinforced plastic is investigated. Chatter analysis may be considered theoretically based on data from impact test of an end mill cutter. However, a stability region obtained in such way may not agree with the real one. Therefore, this paper presents a method that can predict chatter vibrations based on cutting force components measurements. At the beginning, a stability lobe diagram is created to establish the range of experimental test in the plane of tool rotational speed and depth of cut. Next, an experiment of composite milling is performed. The experimentally-measured time series of cutting forces are decomposed with the use of the improved Hilbert–Huang transform (HHT). To detect chatter, statistical methods and recurrence quantification analysis (RQA) are used. However, much better results are obtained when new chatter indexes are proposed. The indexes, derived directly from the HHT and RQA methods, can be used to build an effective chatter prediction system.

Application of Recurrence Quantification Analysis for Non-Linear Dynamical Systems

The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.

Application of Recurrence Quantification Analysis for Non-Linear Dynamical Systems

The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.

Understanding Controller-Pilot Interaction Dynamics in The Context of Air Traffic Control

LiteratureNew capabilities to modernize the U.S. National Airspace System (NAS) include support of real-time information streams derived from many data sources across the NAS. As an emergent proper...

RECURRENCE QUANTIFICATION ANALYSIS OF MCI EEG UNDER RESTING AND VISUAL MEMORY TASK CONDITIONS

The work aims at classifying EEG of mild cognitive impairment (MCI) patients from that of normal control (NC) subjects using recurrence quantification analysis (RQA) and a simple visual memory task, which is commonly used in memory clinics. EEG of MCI and NC groups are recorded under three cognitive conditions, resting eyes closed (EC) and two phases of the task, namely, picture viewing (learning phase, PIC) and picture recollection (immediate free recall phase, PICREC). Complexity analysis of EEG is performed using RQA measures, recurrence rate (RR) and entropy (ENTR). Mean values of these measures over electrodes from four cortical regions are used for statistical analysis of group differences, under the different cognitive conditions. In all the cortical regions, the mean RQA RR and ENTR values of MCI group are observed to be higher compared to NC group under the task conditions. Receiver operating characteristics (ROC) analysis is used for assessing the classification efficiency of the RQA-based method applied to EEG of MCI subjects. A fair classification is obtained in all the four cortical regions during the PIC condition using RR and in all regions except frontal, using ENTR. In the PICREC condition, a good classification is obtained in the temporal, parietal and occipital regions and a fair classification is attained in the frontal region using RR. In this condition, the ENTR values provided a fair classification in all the four cortical regions. These RQA measures are used as feature vectors of SVM classifier to further confirm the classification efficiency of the couplets of RQA RR and ENTR. These results indicate RQA method can efficiently classify MCI EEG based on complexity levels using the simple immediate free recall task.

Nonlinear and Non-Stationary Detection for Measured Dynamic Signal from Bridge Structure Based on Adaptive Decomposition and Multiscale Recurrence Analysis

To test the nonlinearity and non-stationarity of measured dynamic signals from a bridge structure with high-level noise and dense modal characteristics, a method that combines the adaptive signal decomposition with the recurrence analysis is proposed to solve the difficulty of testing nonlinearity and non-stationarity of bridge structure signals. A novel white noise assistance and cluster analysis are introduced to the ensemble empirical mode decomposition to alleviate mode-mixing issues and generate single-mode intrinsic mode functions. Combining the hypothesis-testing scheme of nonstationary and nonlinear synchronization and surrogate techniques, a data-driven recurrence quantification analysis method is proposed and a novel recurrence quantification measure pairs are set up. To demonstrate the efficacy of the proposed methodology, complex signals, which are collected from a carefully instrumented model of a cable-stayed bridge, are utilized as the basis for comparing with traditional nonlinear and non-stationary test methods. Results show that the proposed multiscale recurrence method is feasible and effective for applications to a nonlinear and non-stationary test for real complex civil structures.

Improving sleep/wake classification with recurrence quantification analysis features

Abstract In this work the method of Recurrence Quantification Analysis (RQA), often used for the analysis of complex dynamic systems, is employed to extract novel features for sleep/wake classification only using cardio-respiratory signals like electrocardiogram (ECG), heart rate (HR) and respiratory effort (RE). A polysomnography data set consisting of 313 full-night recordings is used to evaluate the features. The sleep/wake classification is performed with a classifier based on Linear Discriminant Analysis (LDA) and it is validated in a Leave One Subject Out cross-validation (LOSOCV) scheme. More than 1300 features are extracted from five different cardio-respiratory modalities (ECG, RE, HR, and combinations of ECG + RE and RE + HR). Each modality is processed to obtain 13 basic RQA features, five post-processed versions and, furthermore, three normalizations are applied, leading to a total count of 975 RQA features. From literature, 126 known cardio-respiratory and actigraphy features, normalized with the same procedures thus resulting in 378 distinct features, are used for performance comparison. A feature selection method based on the Mahalanobis distance and the inter-feature correlation is used to determine the most relevant features of both sets, resulting in a set of 158 from the existing features (set A) and 232 from the RQA based features (set B). The pooled Cohen’s kappa coefficient for set A and set B is 0.586 and 0.522, respectively. The combination of both feature sets (set C) improves the kappa value to 0.625. In addition, ROC and PR curves with their corresponding Area Under Curve (AUC) values are computed. It is also shown that derived sleep statistics (sleep efficiency, sleep onset, total sleep time, etc.) deviate less with respect to the ground truth annotations using the additional RQA features compared to the use of literature-based features only. Besides the overall performance, the data set is split into six different but almost equally sized age and sex groups to allow unbiased comparisons among each other. It is shown, that the classification performance decreases with increasing age but is almost independent from sex. Hence, for future work it is suggested to implement the RQA features and also to focus on the improvement of features especially for elderly persons.

Epilepsy identification based on EEG signal using RQA method

PurposeEpilepsy is one of the most common neurological diseases and its cause is not unequivocal. Thus, additional methods and searches that may help to diagnose the disease are used in the clinical practice. In this study, we tested the possibility of using the Recurrence Quantification Analysis (RQA) method to identify epilepsy and present the analysis of EEG signals of healthy patients and epileptic patients by the RQA method. Materials/methodsThe recordings of signals belong to 13 patients, which were divided into 2 groups: Group A (5 epileptic patients) and Group B (8 healthy patients). In this study Fp1, Fp2, T3 and T4 electrodes were considered in the analysis using the RQA method. ResultsIt is difficult to explore the dynamics of signals by linear methods. In this study, another way of analyzing the dynamics of signals by the RQA method is presented. The RQA method revealed differences in the dynamics between the epileptic and normal signals, which seemed important in an organoleptic way. It was found that the dynamics of epileptic signals is more periodic than normal signals. To confirm the correctness of the statements issued for the RQA data the Principal Component Analysis mapping was applied. This method showed more clearly the differences in the dynamics of both signals. ConclusionsThe RQA method can be used to identify nonlinear biomedical signals such as EEG signals.

Characterization of high-intensity, long-duration continuous auroral activity (HILDCAA) events using recurrence quantification analysis

Abstract. Considering the magnetic reconnection and the viscous interaction as the fundamental mechanisms for transfer particles and energy into the magnetosphere, we study the dynamical characteristics of auroral electrojet (AE) index during high-intensity, long-duration continuous auroral activity (HILDCAA) events, using a long-term geomagnetic database (1975–2012), and other distinct interplanetary conditions (geomagnetically quiet intervals, co-rotating interaction regions (CIRs)/high-speed streams (HSSs) not followed by HILDCAAs, and events of AE comprised in global intense geomagnetic disturbances). It is worth noting that we also study active but non-HILDCAA intervals. Examining the geomagnetic AE index, we apply a dynamics analysis composed of the phase space, the recurrence plot (RP), and the recurrence quantification analysis (RQA) methods. As a result, the quantification finds two distinct clusterings of the dynamical behaviours occurring in the interplanetary medium: one regarding a geomagnetically quiet condition regime and the other regarding an interplanetary activity regime. Furthermore, the HILDCAAs seem unique events regarding a visible, intense manifestations of interplanetary Alfvénic waves; however, they are similar to the other kinds of conditions regarding a dynamical signature (based on RQA), because it is involved in the same complex mechanism of generating geomagnetic disturbances. Also, by characterizing the proper conditions of transitions from quiescent conditions to weaker geomagnetic disturbances inside the magnetosphere and ionosphere system, the RQA method indicates clearly the two fundamental dynamics (geomagnetically quiet intervals and HILDCAA events) to be evaluated with magneto-hydrodynamics simulations to understand better the critical processes related to energy and particle transfer into the magnetosphere–ionosphere system. Finally, with this work, we have also reinforced the potential applicability of the RQA method for characterizing nonlinear geomagnetic processes related to the magnetic reconnection and the viscous interaction affecting the magnetosphere.

Recurrence Plot and Recurrence Quantification of the Dynamic Properties of Cross-Shaped Laminated Energy Harvester

The paper examines the dynamic properties of bistable cross-shaped laminate plates for broadband energy harvesting applications by converting mechanical vibration energy into the electrical power output. Bistable laminates plates coupled to piezoelectric transducers were excited by application of harmonic excitations and exhibited a range of vibration patterns. The vibration patterns included single-well oscillations and snap-through vibrations of both periodic and chaotic character; such vibration patterns led to a different power output. Classical spectral analysis of measured voltage, displacement and velocity time histories indicated the presence of a variety of nonlinear and chaotic phenomena. As a result, an analysis of the measured displacement and voltage time histories was carried out with the use of the Recurrence Plots and the Recurrence Quantification Analysis methods. The Recurrence Plots method was used for detection of qualitative changes in the dynamic behaviour of the non-linear harvesting system. In order to facilitate interpretation of piezoelectric voltage and laminate displacement, a detailed analysis using Recurrence Plots, Recurrence Quantification Analysis was employed.

Influence of repeated effort induced by a 6-min walk test on postural response in older sedentary women.

According to the latest recommendations, adults should exercise regularly at moderate intensity to improve aerobic fitness and body composition. However, it is unknown whether aerobic exercise at submaximal intensity has detrimental effects on balance in older sedentary adults. We explored the effects of two 6-min walk tests (6MWTs) on the postural responses in 49 sedentary women between 60 and 76years old. We assumed that an increase in the center of pressure (COP) fluctuations or a loss in the complexity of the COP time series would be a sign of a deleterious effect on balance. We used kinematic stabilometric parameters, recurrence quantification analysis (RQA) and the central tendency measure (CTM). We refer to the measures obtained through RQA and CTM methods by dynamical measures. Repeated-measures analysis of variance showed no significant differences between the three sets of postural kinematic measures (before vs. after the first vs. after the second 6MWT). However, we observed significant differences between the three sets for the CTM measure in the antero-posterior direction (p<0.002), RQA determinism in the medio-lateral (ML) direction (p<0.0001), and RQA entropy in the ML direction (F=5.93; p<0.004).Our results indicate that the effects of moderate-intensity walking exercise on posture are not revealed by classical postural kinematic measures but only by dynamical measures. The loss of complexity in the COP time series observed after both the first and second 6MWTs may indicate presymptomatic deterioration in the postural adaptive capabilities of sedentary older women.

Regular and chaotic motion in general relativity: The case of an inclined black hole magnetosphere

Dynamics of charged particles in the vicinity of a rotating black hole embedded in the external large-scale magnetic field is numerically investigated. In particular, we consider a non- axisymmetric model in which the asymptotically uniform magnetic field is inclined with respect to the axis of rotation. We study the effect of inclination onto the prevailing dynamic regime of particle motion, i.e. we ask whether the inclined field allows regular trajectories or if instead, the deterministic chaos dominates the motion. In this contribution we further discuss the role of initial condition, particularly, the initial azimuthal angle. To characterize the measure of chaoticness we compute maximal Lyapunov exponents and employ the method of Recurrence Quantification Analysis.

INFLUENCE OF THE GEOMAGNETIC FIELD ON RECURRENCE QUANTIFICATION EEG INDEXES

The effect of changes of the geomagnetic field on human electroencephalograms (EEG) has been studied. Based on the fact that the relationship between the characteristics of the geomagnetic field and the EEG was not linear, the method of recurrence quantification analysis (RQA) was used. EEGs were recorded in 16 sites of the left and right hemispheres in three background states. Subjects (10 people) participated in the multi-day studies. 9 recurrence indexes (RR, DET, L, DIV, ENTR, RATIO, LAM, TT, CLEAN) were calculated for each of the 16 EEG sites. The correlation coefficients were calculated for each index with the global and local coefficients of geomagnetic activity. Since each recurrence index showed significant individual dynamics in the course of the series, the data for all 9 indexes were averaged. All EEG recurrent indexes showed a reliable correlation with the geomagnetic activity, and the number of correlations in the left hemisphere was significantly bigger than in the right one. A significant excess in the number of correlations in the left temporal area (T3) compared to the right one (T4) has been found. It has been concluded that the geomagnetic field had the most pronounced effect on the temporal lobes.