Nonlinear Signal Processing Techniques Research Articles

Impact of mental arithmetic task on the electrical activity of the human brain

Cognitive neuroscience investigates the intricate connections between brain function and mental processing to understand the cognitive architecture. Exploring the human brain, the epicenter of cognitive activity, offers valuable insights into underlying cognitive processes. To monitor brain states corresponding to various mental activities, appropriate measurement tools are essential. Electroencephalogram (EEG) signals serve as a valuable tool for recording patterns and changes in electrical brain activities. Leveraging non-linear signal processing techniques holds promise for advancing our understanding of brain activities during cognitive tasks. In this study, we analyze the electrical activity of the brain using EEG data collected from subjects engaged in a cognitive workload task. Employing wavelet-based analysis, we capture changes in the structure of EEG signals before and during a mental arithmetic task. Additionally, spectral analysis is conducted to discern alterations in the distribution of spectral contents of EEG signals. Our findings underscore the efficacy of wavelet-based analysis and spectral entropy in quantifying the time-varying and non-stationary nature of EEG recordings, offering effective frameworks for distinguishing between different cognitive activities. Consequently, these methods afford deeper insights into the cognitive architecture by tracking changes in the distribution of the time-varying spectrum.

“Unrest while Resting”? Brain entropy in autism spectrum disorder

Biological systems typically exhibit complex behavior with nonlinear dynamic properties. Nonlinear signal processing techniques such as sample entropy is a novel approach to characterize the temporal dynamics of brain connectivity. Estimating entropy is especially important in clinical populations such as autism spectrum disorder (ASD) as differences in entropy may signal functional alterations in the brain. Considering the models of disrupted brain network connectivity in ASD, sample entropy would provide a novel direction to understand brain organization. Resting state fMRI data from 45 high-functioning children with ASD and 45 age-and-IQ-matched typically developing (TD) children were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database. Data were preprocessed using the CONN toolbox. Sample entropy was then calculated using the complexity toolbox, in a whole-brain voxelwise manner as well as in regions of interests (ROIs) based methods. ASD participants demonstrated significantly increased entropy in left angular gyrus, superior parietal lobule, and right inferior temporal gyrus; and reduced sample entropy in superior frontal gyrus compared to TD participants. Positive correlations of average entropy in clusters of significant group differences scores across all subjects were found. Finally, ROI analysis revealed a main effect of lobes. Differences in entropy between the ASD and TD groups suggests that entropy may provide another important index of brain dysfunction in clinical populations like ASD. Further, the relationship between increased entropy and ASD symptoms in our study underscores the role of optimal brain synchronization in cognitive and behavioral functions.

Median Filtering Detection Based on Quaternion Convolutional Neural Network

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, ... | Find, read and cite all the research you need on Tech Science Press

Nonlinear Signal Processing Method Detects Emotional Changes Induced by Indian Classical Music

Music is one of the major activities that alters the emotional experience of a person. Musical processing in the brain is a complex process involving coordination between various areas of the brain. There are less number of studies that focus on analyzing brain responses due to music using modern signal processing techniques. This research aims to apply a nonlinear signal processing technique i.e. the Recurrence Quantification Analysis (RQA) technique to analyze the brain correlates of happy and sad music conditions while listening to happy and sad ragas of North Indian Classical Music (NICM). EEG signals from 20 different subjects are acquired while listening to excerpts of raga elaboration phases of NICM. Along with behavioural ratings, the signals were analyzed using the Recurrence Quantification Analysis technique. The results showed significant differences in the recurrence plot and recurrence parameters extracted from the frontal and fronto temporal regions in the right and left hemispheres of the brain. Therefore, from the results, it can be concluded that RQA parameters can detect emotional changes due to happy and sad music conditions.

A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

Nonlinear Polynomial Graph Filter for Signal Processing With Irregular Structures

Processing of signals with irregular structures is a fundamental challenge to the conventional signal processing due to the complex structures. Since most of the relationships among such data from engineering applications are inherently nonlinear, those tools that do not take the nonlinearity into account may have limitations in dealing with such relationships. In this paper, a novel nonlinear polynomial graph filter (NPGF) is proposed to facilitate the nonlinear processing of signals with irregular structures. First of all, the nonlinear relationship between the input and output graph signals is constructed, which is further developed to utilize the structure information of the irregular structures (i.e., the graph shift ${\boldsymbol S}$ ). Specifically, the nonlinear kernels and the nonlinear input terms are both designed to be polynomials of the graph shift ${\boldsymbol S}$ . Such nonlinear polynomial relationship is then defined as the NPGF. Note that shift invariance is a fundamental property in signal processing but is not straightforward in the existing nonlinear graph signal processing techniques. Due to the special expression of the NPGF, the shift-invariance is extended to our NPGF, which can conveniently facilitate the analysis and design of graph signals using NPGF and is one of the most important contributions in this paper. Numerical studies that characterize the nonlinear temperature distribution and GDP prediction based on real datasets are presented. Compared with other existing techniques, our NPGF achieves superior capability in characterizing the nonlinear relationship between the input and output graph signals, especially when only very few graph signals can be observed.

The Female Heart: Sex Differences in the Dynamics of ECG in Response to Stress.

Sex differences in the study of the human physiological response to mental stress are often erroneously ignored. To this end, we set out to show that our understanding of the stress response is fundamentally altered once sex differences are taken into account. This is achieved by comparing the heart rate variability (HRV) signals acquired during mental maths tests from ten females and ten males of similar maths ability; all females were in the follicular phase of their menstrual cycle. For rigor, the HRV signals from this pilot study were analyzed using temporal, spectral and nonlinear signal processing techniques, which all revealed significant statistical differences between the sexes, with the stress-induced increases in the heart rates from the males being significantly larger than those from the females (p-value = 4.4 × 10−4). In addition, mental stress produced an overall increase in the power of the low frequency component of HRV in the males, but caused an overall decrease in the females. The stress-induced changes in the power of the high frequency component were even more profound; it greatly decreased in the males, but increased in the females. We also show that mental stress was followed by the expected decrease in sample entropy, a nonlinear measure of signal regularity, computed from the males' HRV signals, while overall, stress manifested in an increase in the sample entropy computed from the females' HRV signals. This finding is significant, since mental stress is commonly understood to be manifested in the decreased entropy of HRV signals. The significant difference (p-value = 2.1 × 10−9) between the changes in the entropies from the males and females highlights the pitfalls in ignoring sex in the formation of a physiological hypothesis. Furthermore, it has been argued that estrogen attenuates the effect of catecholamine stress hormones; the findings from this investigation suggest for the first time that the conventionally cited cardiac changes, attributed to the fight-or-flight stress response, are not universally applicable to females. Instead, this pilot study provides an alternative interpretation of cardiac responses to stress in females, which indicates a closer alignment to the evolutionary tend-and-befriend response.

EVALUATION OF SIGNAL PROCESSING TECHNIQUES IN DISCRIMINATING ECG SIGNALS OF MEN AND WOMEN DURING REST CONDITION AND EMOTIONAL STATES

Great range of electrocardiogram (ECG) signal processing methods can be found in the literature. In addition, the importance of gender differences in physiological activities was also identified in various conditions. This article aims to provide a comprehensive evaluation of linear and nonlinear ECG parameters to indicate suitable signal processing approaches which can show significant differences between men and women. These differences were investigated in two conditions: (i) during rest condition, and (ii) during the affective image inducements. A wide range of parameters from time-, frequency-, wavelet-, and nonlinear-techniques were examined. Applying the Wilcoxon rank sum test, significant differences between two genders were inspected. The analysis was performed on 47 college students at rest condition and while subjects watching four types of affective pictures, including sadness, happiness, fear, and peacefulness. The impact of these emotions on the results was also investigated. The results indicated that 72.95% and 72.61% of all features were significantly different between male and female in rest condition and affective inducements, respectively. In addition, the highest percentage of the significant difference between ECG parameters of men and women was achieved using nonlinear characteristics. Considering all features together, the highest significant difference between two genders was achieved for negative emotions, including sadness and fear. In conclusion, the results of this study emphasized the importance of gender role in cardiac responses during rest condition and different emotional states. Since these gender differences are well manifested by nonlinear signal processing techniques, dynamical gender-specific ECG system may improve the automatic emotion recognition accuracies.

Entropy‐based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise

Non-invasive methods accomplished by a computer aided diagnosis of knee-joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non-linear signal processing technique. This study includes different entropy-based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non-linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy-based features involving approximate entropy, sample entropy, Shannon entropy, Renyi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O ( N ) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non-linear preprocessing and entropy-based features can provide highly distinguishable features for accurate detection of knee-joint disorders.

Application of Hopfield Neural Network for Harmonic Current Estimation and Shunt Compensation

Harmonics generated by nonlinear loads pollute the power system and affect the operation of equipment connected to it. Hence, harmonic mitigation is of prime concern to a power system engineer. Artificial Neural Network (ANN) is a nonlinear signal processing technique, which is built from interconnected elementary processors called neurons. In this article, a Hopfield Neural Network (HNN) based control algorithm for shunt compensator in a power distribution system is realized. The Hopfield network is modeled using energy minimization principle and consists of “n” interconnected neurons. The HNN is used to estimate different harmonic components present in distribution system operating with nonlinear loads. It also provides suitable control signals to the shunt compensator for compensation of various power quality issues such as power factor correction, load balancing, and harmonic reduction in the distribution system. Detailed experimental results are presented along with simulation studies on the prototype model developed in the laboratory and these results demonstrate the feasibility of the proposed method of control in DSTATCOM. The comparison of the HNN-based compensation technique with a popular and effective control algorithm based on Least Means Square (LMS) is also presented in this article.

Advanced MIMO Signal Processing Techniques Enabling Long-Haul Dense SDM Transmissions

Space division multiplexing (SDM) transmission technology is the prime candidate for next technological breakthrough in fiber-optic communication systems, as spatially structured multicore fibers and multimode fibers allow exploitation of the spatial degrees of freedom. Although the use of multi-input multi-output (MIMO) signal processing has significantly contributed to the development of MIMO-SDM transmission, differential mode delay (DMD) and mode dependent loss (MDL) remain as restricting factors of long-haul MIMO-SDM transmission. This paper starts by reviewing the recent progress demonstrated by SDM transmission experiments. Issues with MIMO-SDM transmission including DMD and MDL are then discussed with their methodologies based on optical and digital approaches. Next, brief descriptions of linear and nonlinear MIMO signal processing techniques that enable robust and reliable MIMO-SDM transmission are provided with complexity and performance comparisons, followed by a discussion of their adaptation in coherent MIMO-SDM transmission. We also elucidate the experimental evaluation of frequency selective channels in a multicore few-mode fiber with experimental evaluations, and discuss their impact on MIMO-SDM signal transmission from a signal processing perspective.

Target localization in a reverberant shallow ocean waveguide with environmental uncertainty using a nonlinear frequency-difference signal processing technique

Model-based signal processing for active sonar localization is typically infeasible due to insufficient knowledge of the acoustic environment. Additionally, in a shallow ocean environment, surface or bottom roughness create diffuse reverberation that can often obscure a desired target echo. Recently, a nonlinear signal processing technique was developed for passive acoustic source localization (Worthmann, et al., 2015) that is applicable to high frequency sources in uncertain shallow ocean environments. This technique exploits a nonlinear field product (the autoproduct) and uses in-band hydrophone array measurements to determine field information in a lower, out-of-band, frequency regime where environmental uncertainties are less detrimental. When extended to monostatic active sonar with a vertical array, this technique allows a model-based signal processing algorithm to combat the detrimental effects of reverberation. The nonlinear signal processing algorithm is presented, along with simulations in a 5-km range, 200-m deep ideal waveguide with environmental uncertainties and significant reverberation at frequencies between 2- and 5-kHz. Successful detection and localization of a mid-water-column target is found to be possible at simulated signal-to-reverberation levels as low as −5 dB. Comparisons to existing signal processing detection and localization algorithms are provided. [Sponsored by the Office of Naval Research and the National Science Foundation.]

Nonlinear signal processing for vocal folds damage detection based on heterogeneous sensor network

Heterogeneous sensor network-based medical decision making could facilitate the patient diagnosis process. In this paper, we present an intelligent approach for vocal folds damage detection based on patient׳s vowel voices using heterogeneous sensor network. Based on human voice samples and Hidden Markov Model, we show that transformed voice samples (linearly combined samples) follow Gaussian distribution, further we demonstrate that a type-2 fuzzy membership function (MF), i.e., a Gaussian MF with uncertain mean, is most appropriate to model the transformed voices samples, which motivates us to use a nonlinear signal processing technique, interval type-2 fuzzy logic systems, to handle this problem. We also apply Short-Time-Fourier-Transform (STFT) and Singular-Value-Decomposition (SVD) to the vowel voice samples, and observe that the power decay rate could be used as an identifier in vocal folds damage detection. Two fuzzy classifiers, a Bayesian classifier and a linear classifier, are designed for vocal folds damage detection based on human vowel voices /a:/ and /i:/ only, and the fuzzy classifiers are compared against the Bayesian classifier and linear classifier. Simulation results show that an interval type-2 fuzzy classifier performs the best of the four classifiers.

Interpretations of the frequency difference autoproduct in multipath environments

When locating remote acoustic sources in a shallow ocean sound channel, the established array signal processing technique known as matched field processing (MFP) has shown much success. However, MFP is sensitive to mismatch between the modeled and actual environments, and may fail to localize acoustic sources in the presence of such mismatch, particularly at high frequencies. A recent nonlinear array signal processing technique, frequency difference MFP (Abadi et. al. 2012, Worthmann, et. al., under review), has shown some success in localizing high frequency sources by moving the replica calculations to a lower, out-of-band, difference frequency where the detrimental effects of environmental mismatch are less severe. To extract the requisite out-of-band difference frequency information from the measured signals, a quadratic product, termed the autoproduct, is formed from complex signal amplitudes separated by the difference frequency but still lying within the signal bandwidth. Through the use of simple multipath propagation environments, the nature of this autoproduct is explored, and the reasons that it provides out-of-band field information are presented. More complex propagation environments are simulated as well to demonstrate some of the expected and unexpected behaviors of the autoproduct. [Sponsored by the Office of Naval Research and the National Science Foundation.]

Nonlinear time reversal signal processing techniques applied to acousto-mechanical imaging of complex materials

Recent ten years have seen considerable development of experimental techniques for improving nonlinear NDT methods and harmonic imaging derived from Nonlinear Elastic Wave Spectroscopy (NEWS). Diagnostic ultrasonic imaging based on higher harmonics yields, among others, a better resolution in view of the decreased wavelength in comparison with the fundamental. Furthermore, using symmetry invariance, nonlinear Time Reversal (TR) and reciprocity properties, the classical NEWS methods are supplemented and improved by new excitations having the intrinsic property of enlarging frequency analysis bandwidth and time domain scales. The purpose of this paper is to present the extension of TR-NEWS for skin aging characterization using the Nonlinear Time Reversal signal processing tool known to localize, in a complex medium, sources of nonlinearity potentially responsible of complex material aging. Linear and nonlinear behavior of skin elasticity is measured locally thanks to an acousto-mechanical loading of the skin conducted with INSTRON loading machines specifically optimized for biomaterials. Hysteresis behavior coming from the complex loading of the skin has been identified with PM-space statistical approach, usually associated to aging process in NDT. Phenomenological hysteretic parameters extracted will be presented and associated to standard parameters used for skin characterization.

Flexible and waterproof micro-sensors to uncover zebrafish circadian rhythms: The next generation of cardiac monitoring for drug screening.

Flexible electronics are the next generation of sensors for mobile health and implantation. Zebrafish (Danio rerio) is an emergent strategy for pre-clinical drug development and toxicity testing. To address the confounding effects from sedation of fish and removal from the aquatic habitat for micro-electrocardiogram (µECG) measurements, we developed waterproof and wearable sensors to uncover the circadian variation in heart rate (HR) and heart rate variability (HRV) (Massin et al., 2000). The parylene-C based ECG sensor consisted of an ultra-soft silicone integrated jacket designed to wrap around the fish during swimming. The Young's modulus of this silicone jacket matched with the fish surface, and an extended parylene cable connected the underwater chest electrodes with the out-of water electronics. In addition, embedded micro-glass spheres in the silicone effectively reduced the effective density of the jacket to ~1 g cm(-3). These innovations enabled physiological ECG telemetry in the fish's natural habitat without the need for sedation. Furthermore, a set of non-linear signal processing techniques filtered out the breathing and electromagnetic artifacts from the recorded signals. We observed a reduction in mean HR and an increase in HRV over 24h at 10 dpa, accompanied by QT prolongation as well as diurnal variations, followed by normalization in mean HR and QT intervals at 26 days post ventricular amputation (dpa). We revealed Amiodarone-mediated QTc prolongation, HR reduction and HRV increase otherwise masked by sedation. The novel features of the flexible silicon jacket for µECG telemetry unraveled the biological clock and normalization of QT intervals at 26 dpa, providing the first evidence of new physiological phenomena during cardiac injury and repair as well as cardiac drug-mediated aberrant rhythms. Thus, the light weight and waterproof design holds promise to advance the next generation of mobile health and drug discovery.

Field dimension and skill level constrain team tactical behaviours in small-sided and conditioned games in football

This study analysed the influence of field dimension and players’ skill level on collective tactical behaviours during small-sided and conditioned games (SSCGs). Positioning and displacement data were collected using global positioning systems (15 Hz) during SSCGs (Gk+4 v. 4+Gk) played by two groups of participants (NLP- national-level and RLP-regional-level players) on different field dimensions (small: 36.8 × 23.8 m; intermediate: 47.3 × 30.6 and large: 57.8 × 37.4 m). Team tactical performance was assessed through established dynamic team variables (effective playing space, playing length per width ratio and team separateness) and nonlinear signal processing techniques (sample entropy of distances to nearest opponents and the teams’ centroids’ mutual information). Results showed that the effective playing space and team separateness increased significantly with pitch size regardless of participant skill level (P < 0.001, η2 = 0.78 and P < 0.001, η2 = 0.65, respectively). Playing length per width ratio increased with pitch size for the NLP but was maintained at a relatively constant level by RLP across treatments indicating different playing shapes. There was significantly more irregularity in distances to nearest opponents for the NLP in small (P = 0.003) and intermediate fields (P = 0.01). Findings suggest that tactical behaviours in SSCGs are constrained by field size and skill level, which need to be considered by coaches when designing training practices.

Resting state fMRI entropy probes complexity of brain activity in adults with ADHD

In patients with attention deficit hyperactivity disorder (ADHD), quantitative neuroimaging techniques have revealed abnormalities in various brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Nonlinear signal processing techniques such as sample entropy have been used to probe the regularity of brain magnetoencephalography signals in patients with ADHD. In the present study, we extend this technique to analyse the complex output patterns of the 4 dimensional resting state functional magnetic resonance imaging signals in adult patients with ADHD. After adjusting for the effect of age, we found whole brain entropy differences (P=0.002) between groups and negative correlation (r=−0.45) between symptom scores and mean whole brain entropy values, indicating lower complexity in patients. In the regional analysis, patients showed reduced entropy in frontal and occipital regions bilaterally and a significant negative correlation between the symptom scores and the entropy maps at a family-wise error corrected cluster level of P<0.05 (P=0.001, initial threshold). Our findings support the hypothesis of abnormal frontal–striatal–cerebellar circuits in ADHD and the suggestion that sample entropy is a useful tool in revealing abnormalities in the brain dynamics of patients with psychiatric disorders.

Introduction to Special Issue

The history of chest auscultation by unaided ears goes back to almost 2000 years ago. But the discovery and use of respiratory lung sounds as indicators of health and disease began with Laennec, who formalized the relationship between human pulmonary diseases and respiratory auscultation in 1819. He then invented stethoscope in 1921. Stethoscope enabled physicians to listen to respiratory sounds of their patients and detect any symptomatic signs. Modern technology can help physicians even more these days. Breath sound analysis using digital signal processing techniques has recently drawn much attention because of its diagnostic capabilities. The articles in this special issue focus on how computerized respiratory sound analysis can quantify changes in lung sounds, store records of the measurements made, de-noise the signals of interest from any artifacts and interference, and produce graphical representations of characteristic features of the respiratory sounds to help with the diagnosis and treatment of patients suffering from lung diseases. Ray Murphy reviews the combined use of stethoscope and computerized lung sound analysis in the clinical assessment of lung infectious diseases in the first paper published in this volume. One issue of interest for both physicians and researchers in this field is to find features which best describe the relationship between the respiratory flow and the lung sounds of patients compared to that of healthy individuals. Since lung sounds have relatively low frequency and low intensity, it is crucial to remove the noise and other interfering sounds (i.e., heart sounds) from the lung sounds prior to any diagnostic analysis. Many researchers around the world have been involved in this research applying linear and nonlinear digital signal processing techniques to develop novel methods that remove adventitious sounds form the lung sound record. The paper by Gnitecki and Moussavi reviews a number of heart sound cancellation methods, and the authors discuss some advantages and problems associated with different methods. Adventitious lung sounds that are of diagnostic value include crackles, wheezes, squawks, rhonchi, and stridor. Out of these sounds, detection of crackles (both fine and coarse) is more important because of its diagnostic values at the early stages of disease. Nowadays specialists rely on computer aided analysis to count the number of crackles at different locations of chest. Hence, this area of research is concerned with developing more accurate and efficient methods for detecting crackles. The next two papers by Hadjileontiadis and Charleston et al. present two methods for crackle detection using empirical mode decomposition and fractal dimension analysis.

A Systems Approach to Cardiac Health Diagnosis

With this paper we explore systems engineering as a systematic way of designing a cardiac health visualization system. From a biomedical perspective, the system is based on the well-known fact that Heart Rate (HR) signals indicate the activity of autonomous nervous system and therefore such signals are used to investigate the cardiac health of patients. HR signals are highly nonlinear and non-stationary in nature. Hence, we have extracted the salient features using nonlinear signal processing techniques. In this work, we have analysed 285 subjects from eight different cardiac classes. The features extracted are: Normalized bispectrum entropies (P1 and P2), Approximate Entropy (ApEn), Sample Entropy (SampEn) and Recurrence Entropy (REN). Furthermore, we propose a Cardiac Integrated Index (CII) using different entropies. This one value CII can be used to differentiate normal and abnormal cardiac classes. During the work on signal analysis we realized that proposing and testing of algorithms is done in the requirements phase of the systems design. This is an important realization, because it puts the work on algorithms in perspective to all the design steps necessary to build a physical solution for the important problem of cardiac monitoring.