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Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element’s degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

This paper presents the problem of rolling bearing fault diagnosis based on vibration velocity signal. For this purpose, recurrence plots and quantification methods are used for nonlinear signals. First, faults in the form of a small scratch are intentionally introduced by the electron-discharge machining method in the outer and inner rings of a bearing and a rolling ball. Then, the rolling bearings are tested on the special laboratory system, and acceleration signals are measured. Detailed time-dependent recurrence methodology shows some interesting results, and several of the recurrence indicators such as determinism, entropy, laminarity, trapping time and averaged diagonal line can be utilized for fault detection.

This work investigates the milling of the surface of glass and carbon fiber-reinforced plastics using tools with a polycrystalline diamond insert. The milling process was conducted under three different conditions, namely without the use of a cooling liquid, with oil mist cooling, and with emulsion cooling. The milling process of composites was conducted with variable technological parameters. The variable milling parameters were feed per tooth and cutting speed. The novelty of this work is the use of recurrence methods based on the cutting force signal to analyze the milling of composites with three types of cooling. The primary aim of the study was to determine the effect of variable technological milling parameters on cutting force and to select recurrence quantifications that would be sensitive to the cooling method. It has been shown that recurrence quantifications such as determinism (DET), laminarity (LAM), averaged diagonal length (L), trapping time (TT), recurrence time of the second type (T2), and entropy (ENTR) are sensitive to the cooling methods applied for the tested composite materials. The results have shown that it is possible to determine common ranges of changes in sensitive recurrence quantifications for the two tested variables parameters of milling: 0.63–0.94 (DET), 0.69–0.97 (LAM), 7.30–13.48 (L), 2.92–4.98 (TT), 17.01–38.25 (T2), 2.02–3.16 (ENTR). The ANOVA analysis results have confirmed that the studied variables have a significant impact on the recurrence quantifications.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

Parkinson's disease (PD) is a typical neurodegenerative disorder characterized by progressive motor impairments. Gait analysis offers a promising avenue for non-invasive PD diagnosis, yet extracting discriminative features from gait signals remains challenging. This study proposed an intelligent diagnostic framework for PD based on quantitative analysis of recurrence plots derived from plantar pressure gait signals. Gait data were collected from 61 PD patients and 48 healthy subjects using a wearable gait acquisition system. Various recurrence plots representations, including thresholded, non-thresholded, basic, cross, and joint recurrence plots were constructed. From these recurrence plots, traditional recurrence quantification analysis (RQA) features and novel recurrence plot entropy features derived from compressed one-dimensional sequences of non-thresholded recurrence plots were extracted. Statistical analysis revealed that recurrence plot entropy features particularly from cross recurrence plots exhibited superior discriminability. Pressure signals from the heel and toe positions showed the highest specificity. For classification, an integrated feature set combining temporal, pressure, and recurrence plot features achieved the best diagnostic performance using a Cubic Support Vector Machine (CSVM) model, yielding a maximum accuracy of 92.71% in distinguishing PD patients from healthy controls (HC). The results demonstrated that the proposed quantitative recurrence plots analysis framework provides a highly effective and automated approach for intelligent PD diagnosis based on gait dynamics.

A key emergent property of group social dynamic is synchrony – the coordination of actions, emotions, or physiological processes between group members. Despite this fact and the inherent nested structure of groups, little research has assessed physiological synchronization between group members from a multi-level perspective, thus limiting a full understanding of the dynamics between members. To address this gap of knowledge we re-analyzed a large dataset (N=261) comprising physiological and psychological data that were collected in two laboratory studies that involved two different social group tasks. In both studies, following the group task, members reported their experience of group cohesion via questionnaires. We utilized a nonlinear analysis method-multidimensional recurrence quantification analysis that allowed us to represent physiological synchronization in cardiological interbeat intervals between group members at the individual-level and at the group-level. We found that across studies and their conditions, the change in physiological synchrony from baseline to group interaction predicted a psychological sense of group cohesion. This result was evident both at the individual and the group levels and was not modified by the context of the interaction. The individual- and group-level effects were highly correlated. These results indicate that the relationship between synchrony and cohesion is a multilayered construct. We re-affirm the role of physiological synchrony for cohesion in groups. Future studies are needed to crystallize our understanding of the differences and similarities between synchrony at the individual-level and synchrony at the group level to illuminate under which conditions one of these levels has primacy, or how they interact.

Self-regulated learning (SRL), learners’ monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by prompting and scaffolding learners to engage in SRL via using pedagogical agents. However, current literature does not examine the extent to which learners’ deployed strategies are functional or dysfunctional in relation to pedagogical agent scaffolding. The current study collected 117 undergraduate students’ data as they learned with MetaTutor, an ITS about the human circulatory system. Participants were randomly assigned to either the (1) Prompt and Feedback Condition where pedagogical agents scaffolded cognitive and metacognitive SRL strategies or (2) Control Condition where no prompts or feedback were provided. Results demonstrated that learners who received prompts by the pedagogical agents to engage in SRL had higher learning gains as well as greater frequencies across most strategies compared to those in the Control Condition who relied on self-initiated strategy use. While sequential transitions across all strategies were not significant between conditions, further analysis grounded in Complex Systems Theory found that learners who were prompted to engage in strategies demonstrated a significantly lower degree of repetition and balance between repetitive and novel patterns of strategy use. The findings suggest that pedagogical agents within MetaTutor successfully scaffolded the functional deployment of cognitive and metacognitive SRL strategies and are indicative of higher learning after interacting with ITSs.

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