Fuzzy Recurrence Plots Research Articles

Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Fuzzy Recurrence Plots for Shallow Learning-Based Blockage Detection in a Centrifugal Pump Using Pre-Trained Image Recognition Models

Abstract Rags, dusts, foreign particles, etc., are the primary cause of blockage in the centrifugal pump and deteriorate the performance. This study elaborates an experimental and data-driven methodology to identify suction, discharge, and simultaneous occurrence of both blockages. The discharge pressure signals are acquired and denoised using CEEMD. The fuzzy recurrence plots obtained from denoised signals are attempted to classify using three pre-trained models: Xception, GoogleNet, and Inception. None of these models are trained on such images; thus, features are extracted from different pooling layers which include shallow features too. The features extracted from different layers are fed to four shallow learning classifiers: Quadratic SVM, Weighted k-nearest network, Narrow Neural network, and subspace discriminant classifier. The study finds that subspace discriminant achieves the highest accuracy of 97.8% when trained using features from second pooling of Xception model. Furthermore, this proposed methodology is implemented at other blockage conditions of the pump. The subspace discriminant analysis outperforms the other selected shallow classifier with an accuracy of 93% for the features extracted from the first pooling layer of the Xception model. Therefore, this study demonstrates an efficient method to identify pump blockage using pre-trained and shallow classifiers.

Classification of Motor-Imagery Tasks using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.

Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.

Quantification analysis of fuzzy recurrence plots

The method of fuzzy recurrence plots has been introduced as an extension of the method of recurrence plots to display recurrences of the reconstructed phase-space trajectories of dynamical systems as grayscale images. This paper presents some quantifiable measures of fuzzy recurrence plots in the context of the recurrence quantification analysis.

Recurrence eigenvalues of movements from brain signals

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

Convolutional fuzzy recurrence eigenvalues

The ability to identify characteristics of dynamical systems is important because it allows the understanding and modeling of complex physical phenomena that can be utilized in many applications, ranging from finance, environment, health, to industry. Here the concepts of convolution and pooling in deep learning are introduced to compute the eigenvalues of fuzzy recurrences obtained from the phase-space reconstructions of time series. Computer results show that the largest eigenvalues of convolved fuzzy recurrence plots can differentiate between cohorts of random and chaotic signals.

Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion

Rotating machinery (RM) fault diagnosis based on artificial intelligence (AI) is an esteemed industrial IoT application and an inevitable constituent of the Industry 4.0 revolution. Notably, the existing research in this area exhibits the following limitations: i) the time-series characterization or system dynamics considerations of vibration input are not strictly followed in the diagnostic procedure, ii) fault-specific component usage is not encouraged in existing decision-making strategies, and iii) single classifier based decision-making commonly compromises in utilizing all features of input data. Structural rotor fault (SRF) is the most vital but least attended defect, which is the root cause of most RM issues. We develop a framework that uses two parallel decision-making strategies, i.e., the convolutional neural network (CNN) with fuzzy recurrence plot (FRP) as input and a long short-term memory (LSTM) network taking distinctive frequency components (DFC) stream as input. Moreover, a DFC-based ranking and image combining scheme is proposed to select the most informative sensor signals from the segmented raw vibration signals and generate a 3D-FRP, and 2D flattened FRP (F-FRP). These multi-sensor FRPs characterize the system dynamics and preserve the time-series properties of the vibration input. The DFC stream simultaneously catered to LSTM facilitates symptom parameters-based decision-making. The parallel decision scores are fused by fuzzy integral (FI) based fusion depending on the confidence of the sources. The model performs exceptionally well over the state-of-the-art methods and achieves around 99.0% accuracy with the Meggitt testbed dataset and the MaFaulDa dataset.

A computerized method to assess Parkinson’s disease severity from gait variability based on gender

Parkinson’s disease (PD) is related to dopaminergic neuronal loss and it is progressive. Although there is no available cure for the disease yet, symptom-based treatments are available. PD can be clinically misdiagnosed in early stages because motor features become evident long after the onset of neuronal loss. Therefore, different remote monitoring tests were studied by the scholars for early detection. It has shown that people with PD exhibit gait variability with respect to healthy subjects. In this study, gait signals of PD patients were analyzed to detect severity of PD. Gait signals were converted to fuzzy recurrence plots and deep features were extracted. Machine learning techniques were applied to perform several classification experiments. Binary classifications to discriminate PD patients and multiclass classifications to predict the disease severity based on gender were conducted. Experimental results were assessed with different performance metrics. In PD severity prediction, gender based classification tests produced better performances than the test involving all cases. Proposed system produced state of the art results. The system estimated the disease severity with 1.00 and 0.99 accuracies for females and males respectively.

Classification of short time series in early Parkinson s disease with deep learning of fuzzy recurrence plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson&#x02BC s disease &#x0028 PD &#x0029. A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease. Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long. With an attempt to avoid discomfort to participants in performing long physical tasks for data recording, this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory &#x0028 LSTM &#x0029 neural networks. Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture, fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features

BackgroundIdentifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of the disease. New methodThis paper presents a nonlinear dynamics approach, including recurrence plots, recurrence quantification analysis, fuzzy recurrence plots, and scalable recurrence networks for visualization, classification, and characterization of keystroke time series obtained from healthy control (HC) and early-stage PD subjects. ResultsSeveral differentiative properties for characterizing early PD and HC subjects can be obtained from fuzzy recurrence plots (FRPs) and scalable recurrence networks. Comparison with existing methods: cross-validation results obtained from FRP-based texture are highest among other methods. The method of fuzzy recurrence plots outperforms other existing methods for classification of HC and PD subjects. ConclusionsFeatures extracted from the nonlinear dynamics analysis of the keystroke time series are found to be very effective for machine learning and the properties of the scalable recurrence networks have the potential to be utilized as physiologic markers of the disease.

Texture Classification and Visualization of Time Series of Gait Dynamics in Patients With Neuro-Degenerative Diseases.

The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm is applied to transform gait time series into texture images, which can be visualized to gain insight into disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Experimental results using only the right stride-intervals of the four groups show the effectiveness of the application of the proposed approach.

From fuzzy recurrence plots to scalable recurrence networks of time series

Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data.

Fuzzy recurrence plots

Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.