Recurrence Quantification Analysis Features Research Articles

Recurrence quantification analysis during a mental calculation task.

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

Short-time Fourier transform and embedding method for recurrence quantification analysis of EEG time series

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. Creating features useful for the analysis of the EEG recording can be challenging. Here we introduce a new method of pre-processing the time-series for the analysis of the resting state and binary task classification using recurrence quantification analysis (RQA) and compare it with the existing state-of-the-art approach based on signal embedding. To reveal patterns that unfold brain dynamics, we present a new pipeline that does not rely on selection of embedding parameters for RQA. Instead of using EEG time-series signals directly, Short-term Fourier transform (STFT) is used to generate new time-series, based on the power spectra from sliding, overlapping windows. Recurrence plots are created in a standard way from embedded EEG signals, and the STFT vectors. The efficiency of RQA features extracted from such plots is compared in classification of EEG segments that correspond to open and closed eye conditions. In contrast to the common approaches to such analysis, no filtering into separate frequency bands was needed. Differences between the two representations of EEG signals are illustrated using histograms of RQA features and UMAP plots. Classification results at the 95.9% level were obtained using selected features for less than 10 electrodes.

Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW–RQA features. As validation, the CTW–RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW–RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW–RQA features effectively supplement the existing BCG features for AF detection.

Monitoring the Degradation in the Switching Behavior of a Hydraulic Valve Using Recurrence Quantification Analysis and Fractal Dimensions

Abstract Valves are crucial components of a hydraulic system that enable reliable fluid management. Hydraulic valves actuated by a solenoid are prone to degradation in their switching behavior, which may induce undesirable fluctuations in the fluid pressure and flow rate, thereby impairing the system performance and limiting its predictability and reliability. Therefore, it is imperative to monitor the switching behavior of solenoid-actuated hydraulic valves. First, recurrence quantification analysis (RQA) has been applied to the experimental flow signals from a hydraulic circuit to understand the complex switching behavior of the valve. Using RQA, the monotonicity of six recurrence-based parameters has been assessed. In addition, two more nonlinear features, namely, Higuchi and Katz fractal dimensions have been extracted from the flow signals. Based on these eight features (six RQA-derived features and two nonlinear features) a feature matrix is formulated. Second, in a parallel approach, eight different statistical features are extracted from the flow signal to construct another feature matrix. Subsequently, different machine learning methods namely Ensemble learning, K-Nearest Neighbor (KNN), and support vector machine (SVM) have been trained on these two feature sets to predict the valve switching characteristics. A comparison between two feature sets shows that ensemble learning gives better prediction accuracy (99.95% versus 92.2% using statistical features) when fed with RQA features combined with fractal dimensions. Therefore, this study demonstrates that by utilizing the recurrence plots and machine learning techniques on the flow rate signals, the degradation in the switching behavior of hydraulic valves can be monitored effectively, with a high-prediction accuracy.

Automated Coronary Artery Disease Detection using RQA Features and Quadratic Support Vector Machine

One of the major causes of death globally due to heart disease is the coronary artery disease (CAD). Due to CAD the blood flow to the cardiac muscle is reduced. The progression of this process eventually causes Myocardial Infarction (MI) that result in sudden death. Hence the detection of CAD at early phase is essential. The electrocardiogram (ECG) is mainly used to capture the abnormal cardiac activity for CAD. But the difficulties in manual interpretation of ECG signal leads to error in CAD detection. To overcome the difficulty in CAD diagnostic task, we have proposed a computer aided methodology using the heart rate variability signal (HRV) for auto diagnosis of CAD and Normal heart condition. The hidden characteristics of HRV signal are identified through Recurrence Plot (RP) and the hidden information is quantified by Recurrence quantification analysis (RQA). The extracted RQA based nonlinear features are analyzed for their clinical significance. The set the effective features are used for classification and subjected to three types of Support vector machine (SVM) classifier to discriminate CAD and the normal heart condition. The ECG database of CAD and normal subjects are taken from Physio.net database to obtain the experimental results. The highest diagnostic ability of the classifier is obtained by quadratic SVM with the accuracy of 98.83% where as the linear and cubic SVM classifier provide 97.22 % and 98.37 % classification accuracy respectively.

Intelligent Diagnostics for Bearing Faults Based on Integrated Interaction of Nonlinear Features

An unforeseen fault of the key bearing of production system due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. To improve the reliability of industry production, this article presents an intelligent diagnosis method for element rolling bearing based on the integrated interaction relationship of vibration nonlinear features. The nonlinear features of vibration signals are extracted using recurrence quantification analysis (RQA) and regrouped into different subsets of nonredundant features with the same level of discrimination ability through the technology of ReliefF-affinity propagation clustering. The weighted voting variable predictive model class discrimination (WV-VPMCD) is proposed to fully utilize the interaction of RQA features to do intelligent diagnostics for bearing faults. The experimental results have showed that the WV-VPMCD outperformes the conventional intelligent diagnosis methods in terms of accuracy, consistency, stability, and robustness, especially in the case of small number of samples.

Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.

Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind epileptic disorders. Approximate entropy and recurrence quantification analysis are nonlinear analysis tools to quantify the complexity and recurrence behaviors of non-stationary signals, respectively. Convolutional neural networks are powerful class of models. In this paper, a new method for automatic epileptic electroencephalogram recordings based on the approximate entropy and recurrence quantification analysis combined with a convolutional neural network were proposed. The Bonn dataset was used to assess the proposed approach. The results indicated that the performance of the epileptic seizure detection by approximate entropy and recurrence quantification analysis is good (all of the sensitivities, specificities and accuracies are greater than 80%); especially the sensitivity, specificity and accuracy of the recurrence rate achieved 92.17%, 91.75% and 92.00%. When combines the approximate entropy and recurrence quantification analysis features with convolutional neural networks to automatically differentiate seizure electroencephalogram from normal recordings, the classification result can reach to 98.84%, 99.35% and 99.26%. Thus, this makes automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy.

Determining rhythmicity and determinism of temperature curves in septic and non-septic critically ill patients through chronobiological and recurrence quantification analysis: a pilot study

BackgroundA few studies have demonstrated that critically ill patients exhibit circadian deregulation and reduced complexity of different time series, such as temperature.ResultsIn this prospective study, we enrolled 21 patients divided into three groups: group A (N = 10) included subjects who had septic shock at the time of ICU entry, group B (N = 6) included patients who developed septic shock during ICU stay, and group C consisted of 5 non-septic critically ill patients.Core body temperature (CBT) was recorded for 24 h at a rate of one sample per hour (average of CBT for that hour) and during different occasions: upon ICU entry and exit in groups A and C and upon entry, septic shock development, and exit in group B. Markers of circadian rhythmicity included mean values, amplitude that is the difference between peak and mean values, and peak time. Furthermore, recurrence quantification analysis (RQA) was employed for assessing different markers of complexity of temperature signals.Patients from group C exhibited higher temperature amplitude upon entry (0.45 ± 0.19) in relation with both groups A (0.28 ± 0.18, p < 0.05) and B (0.32 ± 0.13, p < 0.05). Circadian features did not differ within all groups. Temperature amplitude in groups B and C upon entry was negatively correlated with SAPS II (r = − 0.72 and − 0.84, p < 0.003) and APACHE II scores (r = − 0.70 and − 0.63, p < 0.003), respectively, as well as duration of ICU and hospital stay in group B (r = − 0.62 and − 0.64, p < 0.003) and entry SOFA score in group C (r = − 0.82, p < 0.003). Increased periodicity of CBT was found for all patients upon exit related to entry in the ICU. Different RQA features indicating periodic patterns of change of entry CBT were negatively correlated with severity of disease and length of ICU stay for all patients.ConclusionsIncreased temperature rhythmicity during ICU entry was related with lower severity of disease and better clinical outcomes, whereas the more deterministic CBT patterns were found in less critically ill patients with shorter ICU stay.

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.

Mental stress detection using bioradar respiratory signals

Abstract Stress detection techniques seek to provide an accurate assessment of mental health. This paper presents a new stress detection technique based on signals taken by a bioradar. The main advantage of this approach is its non-invasive nature since it uses a non-contact concealed mechanism that does not require the direct interaction between the person and the measuring device. In addition to being one of the first solutions based exclusively on respiratory signals, the novelty of the research also lies in the use of Recurrence Quantification Analysis (RQA) features on respiratory recordings. The RQA features, traditionally applied for heart rate measurements, allowed reaching a precision of 94,4% after the leave-one-subject-out-cross-validation using a multi-layer perceptron.

Volumetric behavior quantification to characterize trajectory in phase space

This paper presents a methodology to extract a number of quantifier features to characterize volumetric behavior of trajectories in phase space. These features quantify expanding and contracting behaviors and complexity that can be used in nonlinear and chaotic signals classification or clustering problems. One of the features is directly extracted from the distance matrix and seven features are extracted from a matrix that is subsequently obtained from the distance matrix. To illustrate the proposed quantifiers, Mackey–Glass time series and Lorenz system were employed and feature evaluation was performed. It is shown that the proposed quantifier features are robust to different initializations and can quantify volumetric behavior characteristics. In addition, the ability of these features to differentiate between signals with different parameters is compared with some common nonlinear features such as fractal dimensions and recurrence quantification analysis features.

DIAGNOSIS OF MULTICLASS TACHYCARDIA BEATS USING RECURRENCE QUANTIFICATION ANALYSIS AND ENSEMBLE CLASSIFIERS

Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non-linearities in the ECG signal can be clearly unraveled using Recurrence Quantification Analysis (RQA) technique. In this paper, RQA features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-Fib, AFL and V-Fib using ensemble classifiers. The clinically significant ([Formula: see text]) features are ranked and fed independently to three classifiers viz. Decision Tree (DT), Random Forest (RAF) and Rotation Forest (ROF) ensemble methods to select the best classifier. The training and testing of the feature set is accomplished using 10-fold cross-validation strategy. The RQA coefficients using ROF provided an overall accuracy of 98.37% against 96.29% and 94.14% for the RAF and DT, respectively. The results achieved evidently ratify the superiority of ROF ensemble classifier in the diagnosis of A-Fib, AFL and V-Fib. Precision of four classes is measured using class-specific accuracy (%) and reliability of the performance is assessed using Cohen’s kappa statistic ([Formula: see text]). The developed approach can be used in therapeutic devices and help the physicians in automatic monitoring of fatal tachycardia rhythms.

Recurrence quantification analysis across sleep stages

Abstract In this work we employ a nonlinear data analysis method called recurrence quantification analysis (RQA) to analyze differences between sleep stages and wake using cardio-respiratory signals, only. The data were recorded during full-night polysomnographies of 313 healthy subjects in nine different sleep laboratories. The raw signals are first normalized to common time bases and ranges. Thirteen different RQA and cross-RQA features derived from ECG, respiratory effort, heart rate and their combinations are additionally reconditioned with windowed standard deviation filters and ZSCORE normalization procedures leading to a total feature count of 195. The discriminative power between Wake, NREM and REM of each feature is evaluated using the Cohen's kappa coefficient. Besides kappa performance, sensitivity, specificity, accuracy and inter-correlations of the best 20 features with high discriminative power is also analyzed. The best kappa values for each class versus the other classes are 0.24, 0.12 and 0.31 for NREM, REM and Wake, respectively. Significance is tested with ANOVA F-test (mostly p

Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification

The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains during dynamic contractions. In golf, both handicap (Hc) and low back pain (LBP) are the main factors associated with the occurrence of injuries. The aim of this study was to analyze the accuracy of support vector machines SVM on EMG-based classification to discriminate Hc (low and high handicap) and LBP (with and without LPB) in the main phases of golf swing. For this purpose recurrence quantification analysis (RQA) features of the trunk and the lower limb muscles were used to feed a SVM classifier. Recurrence rate (RR) and the ratio between determinism (DET) and RR showed a high discriminant power. The Hc accuracy for the swing, backswing, and downswing were 94.4±2.7%, 97.1±2.3%, and 95.3±2.6%, respectively. For LBP, the accuracy was 96.9±3.8% for the swing, and 99.7±0.4% in the backswing. External oblique (EO), biceps femoris (BF), semitendinosus (ST) and rectus femoris (RF) showed high accuracy depending on the laterality within the phase. RQA features and SVM showed a high muscle discriminant capacity within swing phases by Hc and by LBP. Low back pain golfers showed different neuromuscular coordination strategies when compared with asymptomatic.

Identification of resonance states of rotor-bearing system using RQA and optimal binary tree SVM

Aiming to the dynamic nonlinearity of rotor-bearing system in the mechanical and fluid resonance states, a new method combining Recurrence Quantification Analysis (RQA) with optimal binary tree Support Vector Machine (SVM) is proposed for characterizing and identifying the resonance states. RQA is used to obtain the nonlinear characteristic parameters which are able to effectively represent the resonance states without large amount of measurement data. The binary tree SVM is ordered according to the rank of state Mahalanobis distances in the feature vector space. In order to more precisely classify the feature zones, the RQA features are optimally selected as the inputs for each classifier of binary tree SVM by means of the Fish score evaluation. The practical experiments are performed on the cylindrical shaft-journal bearing test rig and the results demonstrate the effectiveness and superiority of the proposed method.

An online sleep apnea detection method based on recurrence quantification analysis.

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM

Automatic detection and classification of epileptic EEG signals have been a significance method for the clinical diagnosis and treatment of epilepsy. The recurrence quantification analysis (RQA) based on the recurrence plot could visualize the recurrence behaviors of dynamical systems from the nonlinear time series and analysis of the recurrence properties. This paper presents a new feature extraction method for epileptic EEG signals based on the recurrence quantification analysis. Experimental results show that the seizure detection directly based on recurrence quantification analysis features has a higher detection performance; especially the classification accuracy based on the deterministic feature can be up to 90.25%. This paper also combines the RQA features with the variation coefficient and fluctuation index, and then puts the feature vectors into a support vector machine (SVM) to automatically detect the epileptic EEG from EEG recordings. Experimental results shows that the proposed methods could achieve a great classification accuracy of 99%.

Detection of Complex Fractionated Atrial Electrograms Using Recurrence Quantification Analysis

Atrial fibrillation (AF) is the most common cardiac arrhythmia but its proarrhythmic substrate remains unclear. Reentrant electrical activity in the atria may be responsible for AF maintenance. Over the last decade, different catheter ablation strategies targeting the electrical substrate of the left atrium have been developed in order to treat AF. Complex fractionated atrial electrograms (CFAEs) recorded in the atria may represent not only reentry mechanisms, but also a large variety of bystander electrical wave fronts. In order to identify CFAE involved in AF maintenance as a potential target for AF ablation, we have developed an algorithm based on nonlinear data analysis using recurrence quantification analysis (RQA). RQA features make it possible to quantify hidden structures in a signal and offer clear representations of different CFAE types. Five RQA features were used to qualify CFAE areas previously tagged by a trained electrophysiologist. Data from these analyzes were used by two classifiers to detect CFAE periods in a signal. While a single feature is not sufficient to properly detect CFAE periods, the set of five RQA features combined with a classifier were highly reliable for CFAE detection.

Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals

Myocardial infarction (MI), also known as a heart attack, is a leading cause of mortality in the world. Spatial vectorcardiogram (VCG) signals are recorded on the body surface to monitor the underlying cardiac electrical activities in three orthogonal directions of the body, namely, frontal, transverse, and sagittal planes. The 3-D VCG vector loops provide a new way to study the cardiac dynamical behaviors, as opposed to the conventional time-delay reconstructed phase space from a single ECG trace. However, few, if any, previous approaches studied the relationships between cardiac disorders and recurrence patterns in VCG signals. This paper presents the recurrence quantification analysis (RQA) of VCG signals in multiple wavelet scales for the identification of cardiac disorders. The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts. This study is strongly indicative of potential automated MI classification algorithms for diagnostic and therapeutic purposes.