Recurrence Quantitative Analysis Research Articles

Non-linear dynamics in ECG: a novel approach for robust classification of cardiovascular disorders

Detecting cardiac disorders from multi-channel ECG has significant implications for cardiac care. Current methods face challenges due to ECG waveform variations by electrode placement, high signal non-linearity, and low millivolt amplitudes. The present study introduces a non-linear analysis approach leveraging Recurrence plot visualizations as the patterned occurrence of well-defined structures, such as the QRS complex, can be exploited effectively using Recurrence plots. Using the Physikalisch-Technische Bundesanstalt dataset from PhysioNet, we examined four cardiac disorder classes- Myocardial infarction, Bundle branch blocks, Cardiomyopathy, Dysrhythmia, and healthy controls, achieving an impressive classification accuracy of 100%. Wilcoxon rank-sum test is performed at 95% C.I. on Recurrence Quantitative Analysis (RQA) features, identifying five features with statistically significant differences across pairs of study groups. Additionally, t-SNE visualizations of latent space embeddings derived from Recurrence plots and RQA features reveal clear separation among cardiac disorders and healthy subjects, underscoring the efficacy of the proposed approach.

Exploring the recurrent states of football teams' tactical organization on the pitch during Brazilian official matches.

Football teams' tactical organization on the pitch is usually represented by the surface area. Considering the different shapes adopted by the teams during the match, the role of the tactical variability for success is lacking. The aim of this study was to explore and to evaluate the association between recurrent states of tactical organization and technical performance during football matches. A total of 28 teams of Brazilian First Division Championships were analysed. Teams' surface area shapes were represented by the maximum value of the Multiscale Fractal Dimension in each timestamp, producing a time series. Recurrences of states of tactical organization were determined via recurrence plots and recurrence quantitative analysis during attacking and defending phases, and considering the whole match. The outcomes were correlated with nine traditional technical performance indicators. The main results showed that structural recurrence or variability on tactical organization is associated with performance success during the defending and attacking actions. Recurrence plot and measures based on the recurrence density proved to be valuable tools to represent teams' dynamics.

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

Electrical-mechanical dynamical coupling between electrocardiographic and photoplethysmographic signals during cardiopulmonary resuscitation

Cardiac arrest (CA) remains a significant cause of death and disability. High-quality cardiopulmonary resuscitation (CPR) can improve the survival rate of CA. A challenging issue is to find physiological indicators for screening and evaluating the cardiovascular function associated with CPR. This study aimed to investigate the electrical-mechanical dynamic coupling between electrocardiographic (ECG) and photoplethysmographic (PPG) signals for indicating cardiovascular function in the progress of CPR. The ECG and PPG signals were simultaneously collected from a porcine CA model (n=10) induced by ventricular fibrillation, and were further divided into four periods: Baseline, CA, CPR, and recovery of spontaneous circulation (ROSC). Recurrence quantitative analysis (RQA) was applied to examine the nonlinear dynamics of the ECG and PPG signals individually, and cross recurrence quantitative analysis (CRQA) was used to examine the ECG-PPG dynamical coupling. The CA influenced the dynamic patterns of electrical and mechanical activities and the electrical-mechanical coupling, which can be observed from the reduced entropy (ENTR) (p < 0.01), reduced determinism (DET) (p < 0.01) and reduced trapping time (TT) (p < 0.01) at CA compared to Baseline. The recurrence rate (RR), ENTR, DET, and TT at CPR were significantly lower than the parameters at ROSC but higher than those at CA. The electrical-mechanical dynamical coupling was sensitive to CPR and able to reflect the changes in cardiac function in the process of CPR.

Towards the extraction of the crystal cell parameters from pair distribution function profiles.

The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%.

Characterisations of self-excited nonlinear oscillations at varied system parameters in a Rijke tube with a premixed laminar flame

Characterisations of self-excited nonlinear oscillations at varied system parameters in a Rijke tube with a premixed laminar flame

Delamination Depth Evaluation in Near-Surface of GFRP Laminates Based on RQA-MKLSVM Model

The increasing use of glass fiber reinforced polymer (GFRP) laminates in aircraft creates an urgent demand for nondestructive testing to identify defects such as impact damage and delamination in the materials’ near-surface structure. The High-frequency ultrasonic technique is an effective nondestructive testing technique capable of detecting such defects. However, with the increased delamination depth, it is difficult to accurately characterize the delamination defects in GFRP laminates by traditional characterization methods. And the conventional defect depth detection method takes much time and requires professional discrimination, which is easily affected by the level of inspectors and subjective factors. To address the issue of inaccurate feature extraction and low detection efficiency, we propose a new method for delamination depth recognition in the near-surface of GFRP laminates based on the RQA-MKLSVM model by combining the recurrence quantitative analysis (RQA) feature extraction method with the multi-kernel learning SVM technique to improve the capability of detecting delamination defects on GFRP laminates. Additionally, an image-enhanced method based on the nonlinear transformation function and wavelet multiscale product is used to locate the delaminated areas. Results show that the proposed method achieves accurate recognition of different delamination defects within depths ranging from 0.8 mm to 4.0 mm, and the average recognition rate has reached 95.63%. Compared with the training models based on discrete wavelet transform(DWT) and empirical mode decomposition (EMD), the recognition rate of our method is improved by 6.25% and 4.38%, respectively. And Compared with the two single-core models, the recognition accuracy of our method is improved by 8.55% and 5.00%, respectively.

Experimental fault detection of rotating machinery through chaos-based tools of recurrence plot and recurrence quantitative analysis

Experimental fault detection of rotating machinery through chaos-based tools of recurrence plot and recurrence quantitative analysis

Recurrence quantitative analysis of heart rate variability during intradialytic hypotension

Recurrence quantitative analysis of heart rate variability during intradialytic hypotension

Effects of intake charge temperature and relative air-fuel ratio on the deterministic characteristics of cyclic combustion dynamics of a HCCI engine

Homogenous charge compression ignition (HCCI) combustion can significantly reduce automotive pollution and increase the thermal efficiency of the engine. However, combustion phasing control is a major challenge in HCCI engines due to severe cyclic combustion variations. This study investigates the cyclic combustion dynamics of the HCCI engine using nonlinear dynamic methods such as return maps, recurrence plots (RPs), and recurrence quantitative analysis (RQA). Combustion stability and cyclic variations of HCCI combustion parameters were investigated on a modified four-stroke diesel engine. The experiments were conducted by varying relative air-fuel ratios ([Formula: see text]) and intake air temperatures ([Formula: see text]) at two engine speeds. In-cylinder pressure data of 2000 consecutive engine combustion cycles is logged for each test condition. In this study, deterministic characteristics of combustion phasing (CA50) and crank angle position of maximum cylinder pressure ([Formula: see text]) are investigated and compared by employing nonlinear dynamical methods. Return maps revealed that [Formula: see text] is having distinct and more frequently observed deterministic characteristics in comparison to CA50. Patterns in RPs showed a more persistent and sudden change in the combustion dynamics at higher engine speeds. Recurrence plot-based analysis found the existence of deterministic features in the combustion dynamics irrespective of the operating conditions. It was found using RQA parameters that the deterministic nature becomes stronger with a decrease in [Formula: see text] and any deviation in intermediate values of [Formula: see text]. Additionally, RQA measures advocate that CA50 has more deterministic characteristics at higher engine speed while [Formula: see text] at lower engine speed. Strong coupling and synchronization between [Formula: see text] and CA50 is indicated by cross-recurrence plots and CPR index when engine operated with a comparatively richer mixture.

Measuring Real-Time Medication Effects From Electroencephalography.

Evaluating the effects of antiseizure medication (ASM) on patients with epilepsy remains a slow and challenging process. Quantifiable noninvasive markers that are measurable in real-time and provide objective and useful information could guide clinical decision-making. We examined whether the effect of ASM on patients with epilepsy can be quantitatively measured in real-time from EEGs. This retrospective analysis was conducted on 67 patients in the long-term monitoring unit at Boston Children's Hospital. Two 30-second EEG segments were selected from each patient premedication and postmedication weaning for analysis. Nonlinear measures including entropy and recurrence quantitative analysis values were computed for each segment and compared before and after medication weaning. Our study found that ASM effects on the brain were measurable by nonlinear recurrence quantitative analysis on EEGs. Highly significant differences ( P < 1e-11) were found in several nonlinear measures within the seizure zone in response to antiseizure medication. Moreover, the size of the medication effect correlated with a patient's seizure frequency, seizure localization, number of medications, and reported seizure frequency reduction on medication. Our findings show the promise of digital biomarkers to measure medication effects and epileptogenicity.

Application of delay embedding and recurrence analysis to a noisy nonlinear map for cyclic combustion dynamics of SI engines

A noisy nonlinear dynamical (NND) model available for cyclic variations in SI engines is examined using nonlinear time series methods. Heat release time series computed from this model is analyzed by employing bifurcation diagrams, return maps, recurrence plots, and recurrence quantitative analysis techniques. The analysis methodology is at first applied to a noisy Logistic map to optimize the necessary parameters, and later the same is used for the NND map. The presence of noise hides the local patterns of the dynamics and higher periodic orbits, making the bifurcation diagrams look fuzzy for both maps. An increase in the noise level and uncombusted residuals in the NND map advance the onset point of bifurcations toward a higher equivalence ratio. The application of the NND model is extended to represent the SI engine combustion dynamics for a higher amount of internal EGR as it is favored for cycle-resolved control due to its feedback period of one combustion cycle. The combined effect of lean charge and internal EGR leads to a more complex dynamical nature. The addition of a higher amount of internal EGR adds some complex chaotic-like regions in the bifurcation diagrams. For a low level of fluctuations in the NND map, more than four noisy periodic orbits are observed for such operating conditions. The geometry of phase space trajectories for the low-level noisy model data with a high level of internal EGR resembles a chaotic system. Bifurcation diagrams, recurrence plots, and quantitative recurrence analysis indicate an intermittent route to chaos at a highly lean and diluted charge. The presence of deterministic chaotic characteristics in combustion dynamics, along with effort-saving techniques of data acquisition and estimating combustion phasing, depicts the possibilities of practical application in active onboard control of engine systems.

Investigation of bifurcations in cyclic combustion dynamics of a CNG-diesel RCCI engine

This study uses nonlinear dynamical and chaotic methods to investigate the cyclic combustion dynamics of a CNG-Diesel reactivity control compression ignition (RCCI) engine. The experiments of RCCI combustion mode are conducted on a single-cylinder automotive diesel engine with the development ECU. Low reactivity fuel (i.e., CNG) is injected into the intake manifold, and high reactivity fuel (i.e., diesel) is injected directly into the engine cylinder during the RCCI experiments. Mass of fuel injection per cycle and their injection events are controlled by using ECU. The engine is tested for fixed engine load and speed of 3 bar BMEP and 1500 rpm, respectively. A double diesel injection strategy is used for injecting the diesel fuel into the engine cylinder. In-cylinder pressure is measured using a piezoelectric pressure transducer on a crank angle basis with a resolution of 0.1 CAD. Combustion parameters are calculated on a cyclic basis from measured in-cylinder pressure. In this study, the effect of diesel injection timing (SOI-2) on the behavior of combustion dynamics for two different masses of port-injected CNG fuel (mc) is investigated. The CA50 time series of 1000 consecutive engine cycles is analyzed for combustion dynamics analysis using symbol sequence analysis, return maps, recurrence plots (RPs), recurrence quantitative analysis (RQA), and 0–1 test. The peaks in symbol sequence histograms and the corresponding symbol series depict the period-2 nature of the cyclic combustion dynamics at the intermediate SOI-2 of 30° and 40° bTDC. Return maps also confirm the onset of noisy period-2 bifurcation at 30° bTDC SOI-2. The dominance of deterministic period-2 characteristics is found in the cyclic combustion dynamics at 30° and 40° bTDC SOI-2 using recurrence plots. The 0–1 test method depicts stronger chaotic cyclic combustion dynamics at 20° and 50° bTDC SOI-2. In summary, the present study supports the existence of a periodic window in between the chaotic combustion for the intermediate values of SOI-2. The dominance of the deterministic characteristics makes this regime of intermediate SOI-2 values suitable for a better cycle resolved control.

Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques

Abstract Rolling element bearings are one of the most common mechanical components used in a wide variety of rotating systems. The performance of these systems is closely associated with the health of bearings. In this study, a nonlinear time series analysis method, i.e., recurrence analysis is utilized to assess the health of bearings using time domain data. The recurrence analysis acquires the quantitative measures from the recurrence plots and provides an insight to the system under investigations. Experiments are performed to generate the vibration data from the healthy and faulty bearing. Eight recurrence quantitative analysis measures and five time-domain measures are used for the investigations. Three artificial intelligence techniques: rotation forest, artificial neural network, and support vector machine are employed to quantify the diagnosis performance. Results highlight the ability of recurrence analysis to identify the health state of the bearing at the early stage and superior diagnosis accuracy of the proposed methodology.

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability.

Exploring the presence of nonlinearity through surrogate data testing provides insights into the nature of physical and biological systems like those obtained from heart rate variability (HRV). Short-term HRV time series are of great clinical interest to study autonomic impairments manifested in chronic diseases such as the end stage renal disease (ESRD) and the response of patients to treatment with hemodialysis (HD). In contrast to Iterative Amplitude Adjusted Fourier Transform (IAAFT), the Pinned Wavelet Iterative Amplitude Adjusted Fourier Transform (PWIAAFT) surrogates preserve nonstationary behavior in time series, a common characteristic of HRV. We aimed to test synthetic data and HRV time series for the existence of nonlinearity. Recurrence Quantitative Analysis (RQA) indices were used as discriminative statistics in IAAFT and PWIAAFT surrogates of linear stationary and nonstationary processes. HRV time series of healthy subjects and 29 ESRD patients before and after HD were tested in this setting during an active standing test. Contrary to PWIAAFT, linear nonstationary time series may be erroneously regarded as nonlinear according to the IAAFT surrogates. Here, a lower proportion of HRV time series was classified as nonlinear with PWIAAFT, compared to IAAFT, confirming that the nonstationarity condition influences the testing of nonlinear behavior in HRV. A contribution of nonlinearity was found in the HRV data of healthy individuals. A lower proportion of nonlinear time series was also found in ESRD patients, but statistical significance was not found. Although this proportion tends to be lower in ESRD patients, as much as 60% of time series proved to be nonlinear in healthy subjects. Given the important contribution of nonlinearity in HRV data, a nonlinear point of view is required to achieve a broader understanding of cardiovascular physiology.

Cardiac and Autonomic Dysfunctions Assessed Through Recurrence Quantitative Analysis of Electrocardiogram Signals and an Application to the 6-Hydroxydopamine Parkinson's Disease Animal Model.

A classic method to evaluate autonomic dysfunction is through the evaluation of heart rate variability (HRV). HRV provides a series of coefficients, such as Standard Deviation of n-n intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD), which have well-established physiological associations. However, using only electrocardiogram (ECG) signals, it is difficult to identify proper autonomic activity, and the standard techniques are not sensitive and robust enough to distinguish pure autonomic modulation in heart dynamics from cardiac dysfunctions. In this proof-of-concept study we propose the use of Poincaré mapping and Recurrence Quantification Analysis (RQA) to identify and characterize stochasticity and chaoticity dynamics in ECG recordings. By applying these non-linear techniques in the ECG signals recorded from a set of Parkinson’s disease (PD) animal model 6-hydroxydopamine (6-OHDA), we showed that they present less variability in long time epochs and more stochasticity in short-time epochs, in their autonomic dynamics, when compared with those of the sham group. These results suggest that PD animal models present more “rigid heart rate” associated with “trembling ECG” and bradycardia, which are direct expressions of Parkinsonian symptoms. We also compared the RQA factors calculated from the ECG of animal models using four computational ECG signals under different noise and autonomic modulatory conditions, emulating the main ECG features of atrial fibrillation and QT-long syndrome.

Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification analysis

Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.

Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia

Schizophrenia is a very serious chronic disease with very high hazards. Its pathogenesis is still unclear, and searching for its potential biomarkers has always been a popular issue in the field of schizophrenia research. Nonlinear dynamic analysis of magnetoencephalogram (MEG) signals in patients with schizophrenia is helpful to solve this problem. To quantitatively analyse multiscale recurrence information of MEG signals in patients with schizophrenia, this investigation propose a novel approach termed the multiscale multidimensional recurrence quantitative analysis (MsMdRQA). We first employed two sets of model series, which were generated by Lorenz and Rössler systems, to test the effectiveness of MsMdRQA in nonlinear analysis and to analyse the effects of dimension. Then we applied MsMdRQA to analyse MEG signals in schizophrenia. The test results suggest that both the Lorenz system and the Rössler system have different recurrence states at different scales; therefore, they effectively reflect the internal structure information of the Lorenz system and the Rössler system. Furthermore, we found that the recurrence values of the MEG signals in patients with schizophrenia were significantly lower (p < 0.001) than those of healthy control subjects. Overall, MsMdRQA provides a better analysis method for exploring the nonlinear characteristics of complex systems with different scales.

Negative Emotion Management Using a Smart Shirt and a Robot Assistant

Negative affects such as anger, fear, nervousness, depression, etc., may increase human's susceptibility to illness. In this letter, we propose a negative emotion management system that is able to recognize negative emotions through ECG signal and perform emotion regulation through a robot assistant, which has a potential for reducing health risks. A smart shirt is developed to collect the ECG signal from the human body. The robot assistant has the ability to engage in verbal conversations with humans. Recurrence Quantitative Analysis (RQA) is used to extract ECG features for emotion classification purpose. Along with our own dataset, two other public datasets, RECOLA and DECAF, are also used to evaluate our methodology. The detection of negative emotion can trigger the robot assistant to help the user get out of such situations through interactive conversations. We tested and evaluated the proposed framework through experiments. We also assessed the effectiveness of the interactions with the robot on the emotional well-being of older adults.

Milling chatter monitoring under variable cutting conditions based on time series features

Chatter monitoring is an important task while ensuring machining quality and improving machining efficiency. Under variable cutting conditions, cutting parameters often change, and the cutting state and traditional time-frequency domain cutting features will also change significantly. In order to achieve precise monitoring of chatter under such cutting conditions, this article proposes a time series method named recurrence plot (RP) that can reflect the non-stationary characteristics and state differences of the signal system to analyze the cutting force signal in the cutting process. Firstly, reconstruct the phase space, generate RP; then use recurrence quantitative analysis (RQA) to extract statistical features in the RP that reflect the current state of the system; thirdly, use affinity propagation (AP) clustering method to find out the exemplar features from the RQA; finally, train the exemplar features using the light gradient boosting (LGB) method to obtain the classification prediction model. Experimental results show that this method can effectively identify the machining chatter state and stable cutting state under variable cutting conditions.