Recurrence Quantification Analysis Parameters Research Articles

Nonlinear analysis of electromyogram following gait training with myoelectrically triggered neuromuscular electrical stimulation in stroke survivors

Neuromuscular electrical stimulation (NMES) facilitates ambulatory function after paralysis by activating the muscles of the lower extremities. The NMES-assisted stepping can either be triggered by a heel-switch (switch-trigger), or by an electromyogram (EMG)-based gait event detector (EMG-trigger). The command sources—switch-trigger or EMG-trigger—were presented to each group of six chronic (>6 months post-stroke) hemiplegic stroke survivors. The switch-trigger group underwent transcutaneous NMES-assisted gait training for 1 h, five times a week for 2 weeks, where the stimulation of the tibialis anterior muscle of the paretic limb was triggered with a heel-switch detecting heel-rise of the same limb. The EMG-trigger group underwent transcutaneous NMES-assisted gait training of the same duration and frequency where the stimulation was triggered with surface EMG from medial gastrocnemius (MG) of the paretic limb in conjunction with a heel-switch detecting heel-rise of the same limb. During the baseline and post-intervention surface EMG assessment, a total of 10 s of surface EMG was recorded from bilateral MG muscle while the subjects tried to stand steady on their toes. A nonlinear tool—recurrence quantification analysis (RQA)—was used to analyze the surface EMG. The objective of this study was to find the effect of NMES-assisted gait training with switch-trigger or EMG-trigger on two RQA parameters—the percentage of recurrence (%Rec) and determinism (%Det), which were extracted from surface EMG during fatiguing contractions of the paretic muscle. The experimental results showed that during fatiguing contractions, (1) %Rec and %Det have a higher initial value for paretic muscle than the non-paretic muscle, (2) the rate of change in %Rec and %Det was negative for the paretic muscle but positive for the non-paretic muscle, (3) the rate of change in %Rec and %Det significantly increased from baseline for the paretic muscle after EMG-triggered NMES-assisted gait training. Therefore, the study showed an improvement in paretic muscle function during a fatiguing task following gait training with EMG-triggered NMES. This study also showed that RQA parameters—%Rec and %Det—were sensitive to changes in paretic/non-paretic muscle properties due to gait training and can be used for non-invasive muscle monitoring in stroke survivors undergoing rehabilitation.

RECURRENCE QUANTIFICATION ANALYSIS OF BODY RESPONSE TO FUNCTIONAL ELECTRICAL STIMULATION ON HEMIPLEGIC SUBJECTS

Hemiplegia means paralysis of half of the body. It commonly occurs following "stroke", which is due to impedance of blood supply to the brain, hence also termed as "cerebrovascular accident" (CVA). As a consequence of this, the brain tissues suffer from ischemic damage resulting in several symptoms, such as mere weakness, numbness to complete loss of power (paralysis). In order to restore or improve the lost functional movement of the body of the stroke-affected and hemiplegic subjects, a method called functional electrical stimulation (FES) has often been employed as the measure of rehabilitation. FES makes use of low levels of electrical current to activate the nerves and then the muscles, affected. The response of the body to this electrically triggered nervous stimulation could be recorded through different bio-signals. In our work, we measured the accelerometers of hemiplegic patient in two states; with FES and without FES. The nonlinear and nonstationary walking-function-related accelerometers are analyzed using recurrence plots (RP), which helps to visualize the dynamic behavior of the signals. The RPs of electromyography (EMG) signals with stimulation showed distinct periodicity and rhythm when compared to that without stimulation. In addition, we extracted recurrence quantification analysis (RQA) parameters from RP to quantify the obtained information from the RP. Lower values were observed for most of the RQA parameters with FES than obtained without FES. This also confirmed the fact that FES is very useful in bringing more order, rhythm and better control in the physical activities of hemiplegic people.

A Note on Implementing Recurrence Quantification Analysis for Network Anomaly Detection

This paper deal with the network anomaly detection, based on the analysis of non-stationary properties that occur in the aggregated IP traffic flows. We use recurrence quantification analysis (RQA), a mathematical nonlinear technique to achieve this task. The objective is to model the standard network traffic and report any deviation from it. We create a baseline from which we derive the RQA parameters. Using these parameters we explore the hidden recurrence patterns in the network traffic. Further, the detection is analysed using the support vector machine to classify the deviations from the regular traffic. Experiments are conducted on Vellore Institute of Technology University campus network traffic data to validate the model. Defence Science Journal, 2012, 62(2), pp.112-116 , DOI:http://dx.doi.org/10.14429/dsj. 62.1171

Prediction of Surface Finish and Optimization of Machining Parameters in Turning

Surface roughness plays a crucial role in the functional capacity of machined parts. In this work, experiments were carried out on a conventional lathe for different cutting parameters namely feed, spindle speed, depth of cut and tool nose radius according to Taguchi Design of Experiments. Radial acceleration readings were taken with an accelerometer. Optimum cutting parameters and their level of significance were found using Taguchi analysis (ANOVA). Regression analysis was carried out to identify whether the experimental roughness values have fitness characteristic with the process parameters. Recurrence Plots (RP) were obtained using the sensor signals which determine surface roughness qualitatively and Recurrence Quantification Analysis (RQA) technique was used to quantify the RP obtained. Surface finish was predicted using a feed forward back propagation neural network with RQA parameters, cutting parameters and acceleration data as inputs to the network. The validity and reliability of the methods were verified experimentally.

Study of transition velocity from bubbling to turbulent fluidisation by recurrence plots analysis on pressure fluctuations

AbstractRecurrence plot (RP) and recurrence quantification analysis (RQA) have been used for studying the dynamic behaviour of gas–solid fluidised beds. RQA variables (recurrence rate, determinism, laminarity and entropy) were calculated for the bubbling fluidised bed at different superficial gas velocities (ranging 0.1–1.5 m/s) and aspect ratios (1, 1.5 and 2). Patterns within RP changed with these various conditions, and subsequently RQA parameters changed accordingly. These changes can be potentially useful to study the bed hydrodynamic. It was shown that transition from bubbling to turbulent velocity can be determined by the variation of recurrence rate and entropy. © 2011 Canadian Society for Chemical Engineering

Study of traditional Chinese medicine pulse signals in patients with coronary heart disease based on recurrence quantification analysis

By using recurrence quantification analysis (RQA) to analyze traditional Chinese medicine pulse signals of patients with coronary heart disease (CHD), this study aims to find nonlinear dynamic parameters of pulses to distinguish patients with CHD from normal subjects. First, pulse signals were collected using ZBOX-I pulse digitization gathering analyzer from October 2007 to June 2008. RQA was used to analyze RQA parameters of pulses of 63 patients with CHD and 61 normal subjects. RQA parameters included recurrence rate (RR), determinism (DET), averaged diagonal length (L), entropy of diagonal length (ENTR), length of longest diagonal line (L(max)), laminarity (LAM), trapping time (TT) and length of longest vertical line (V(max)). Then, rank-sum test and BoxPlot were employed to find significant difference and distribution of RQA parameters. Lastly, receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the measurements with significant difference. There were significant differences in RQA parameters of pulse signals between the two groups, including RR, DET, L, ENTR, LAM, TT and V(max), and their areas under the ROC curves were 1.000, 0.898, 0.653, 0.673, 0.885, 0.898, 0.986 and 0.994, respectively. Compared with the normal subjects, the pulse signals of the patients with CHD are presented with more certainty, regularity and stability. RQA measurements of RR, TT, Vmax, DET and LAM show good diagnostic value according to their ROC curves.

APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

A Method to Analyze Power System Quality Disturbing Signal Based on Recurrence Quantification Analysis

Recurrence plot and recurrence quantification analysis (RQA) are introduced in the research of nonstationary power disturbance signal process in this thesis. Through analyzing recurrence plot of power quality disturbance signal, the recurrence phenomena are observed within different patterns of PDS internal dynamic mechanism. The recurrence characteristics are shown by RQA parameters quantitatively. With the various parameters taken through RQA, the different power disturbance patterns are differentiated. For PDS end point inspection, signal recurrence level is the characteristic parameter which is used for measuring the recurrence frequency of vector points and the aggregation level of the tracks in system phase space. Based upon this parameter, the differences of power disturbance dynamic characteristics are analyzed, and the PDS end points are also inspected. The result of simulation experiments shows that the identification and positioning of various transient disturbance signals

Recurrence quantification analysis of sustained sub-maximal grip contractions in patients with various metabolic muscle disorders

Recurrence quantification analysis (RQA) was used to analyse force signals during sustained sub-maximal grip contraction (SSGC) of three types of patients suffering from a metabolic muscle disorder (glycogen storage disease type III (GSD III), glycogen storage disease type V (GSD V) and mitochondrial myopathies (MITO)) compared to control subjects. Recurrence plots (RP) of patients showed clear non-uniformity, in comparison to control subjects who displayed quasi-periodic patterns. Quantitative analysis of the RP showed significant differences between patients with metabolic disorders and the control group for four RQA parameters. The results showed that the SSGC signals of patients had decreased L max, which indicated more chaotic patterns. In addition the deterministic component of the signals was less complex for patients than for controls. The differences of SSGC signal observed using RP and RQA were possibly related to the underlying changes in metabolism of muscle fibres due to the disease. Results of this study illustrate that the RQA technique is well suited to analyse sustained grip-force signals.

Investigation on feasibility of recurrence quantification analysis for detecting flank wear in face milling

Flank wear is a critical phenomenon which has direct impact on quality of surface finish, dimensional precision and ultimately cost of the finished product. In any metal cutting operation, cutting tool wear estimation will help in identifying tool state, which is a critical factor in productivity. In this paper, the vibration signals are used for detecting flank wear in face milling .The vibration signals are analyzed using a novel non linear technique called recurrence quantification analysis (RQA). RQA includes time delay and dimensions embedding process so as to reconstruct the time series data of vibration signal, to obtain better information of the changes in nonlinear dynamics underlying the milling process. An investigation of this technique was carried out to see its capability in detecting flank wear in face milling. Experiments were conducted on universal milling machine using AISI H11 steel as work material. The investigation proved that RQA technique has a good potential in detecting flank wear in face milling. The RQA parameters such as percent recurrence (REC), trapping time (TT), percent laminarity (LAM) and entropy (ENT), and also the recurrence plots color patterns for different flank wear, can be used in detecting insert wear in face milling. Keywords: milling, flank wear, recurrence plot, recurrence quantification analysis.

Recurrence Quantification Analysis of Spontaneous Electrophysiological Activity during Development: Characterization of In Vitro Neuronal Networks Cultured on Multi Electrode Array Chips

The combination of a nonlinear time series analysis technique, Recurrence Quantification Analysis (RQA) based on Recurrence Plots (RPs), and traditional statistical analysis for neuronal electrophysiology is proposed in this paper as an innovative paradigm for studying the variation of spontaneous electrophysiological activity of in vitro Neuronal Networks (NNs) coupled to Multielectrode Array (MEA) chips. Recurrence, determinism, entropy, distance of activity patterns, and correlation in correspondence to spike and burst parameters (e.g., mean spiking rate, mean bursting rate, burst duration, spike in burst, etc.) have been computed to characterize and assess the daily changes of the neuronal electrophysiology during neuronal network development and maturation. The results show the similarities/differences between several channels and time periods as well as the evolution of the spontaneous activity in the MEA chip. RPs could be used for graphically exploring possible neuronal dynamic breaking/changing points, whereas RQA parameters are suited for locating them. The combination of RQA with traditional approaches improves the identification, description, and prediction of electrophysiological changes and it will be used to allow intercomparison between results obtained from different MEA chips. Results suggest the proposed processing paradigm as a valuable tool to analyze neuronal activity for screening purposes (e.g., toxicology, neurodevelopmental toxicology).

Recurrences in heart rate dynamics are changed in patients with diabetes mellitus

Detection of subclinical autonomic dysfunction in patients with diabetes mellitus (DM) is of vital importance for risk stratification and subsequent management. Heart rate variability (HRV) analysis is a sensitive tool for assessment of cardiovascular autonomic dysfunction. As the heart is controlled by non-linear deterministic system, the non-linear dynamics measures should be preferred. Recurrence plot (RP) is able to analyse recurrences within system dynamics. The aim of the study was to detect heart rate dysregulation in DM by RP and to ascertain which of the recurrence quantification analysis (RQA) measures are changed in patients with DM compared to control group. We analysed HRV recordings from 17 young patients with type 1 DM and 17 healthy matched control subjects. RQA was performed on RPs with a fixed value of recurrence points percentage. From RQA measures based on diagonal lines, we have found higher percentage of determinism in DM group (P=0.038). Trapping time measure was also higher in DM (P=0.022). RQA revealed changes in dynamics recurrences with reduced complexity of heart rate control in young diabetic patients. As RQA parameters are independent of overall HRV, parameters of RP should be used together with linear HRV parameters for better description of heart rate dysregulation in patients with diabetics.

Characterization of regime shifts in environmental time series with recurrence quantification analysis

A non-linear time series analysis technique, recurrence quantification analysis (RQA) based on recurrence plots (RP), is proposed in this paper for the characterization of regime shifts in environmental time series. Its application is illustrated for two case studies: lake eutrophication by excessive phosphorous and the regime shift that occurred in Ringkøbing Fjord when water exchange with the North Sea was modified. The results show that RQA is robust against high noise levels (up to 100%) and may be easily implemented on-line. In addition, oxygen dynamics in a Mediterranean coastal lagoon (Sacca di Goro) is also analyzed and the results compared with surrogate time series with oxygen concentration at saturation. RPs could be used for graphically exploring possible thresholds or breaking points, whereas RQA parameters are suited for locating them. The main drawback is the need of a constant sampling frequency with no gaps, which may be problematic for some environmental time series.