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The looseness monitoring of bolted joints is a significant issue to ensure structural integrity and safety in the industrial field. This paper proposes a novel approach to monitor bolt looseness based on piezoelectric active sensing. During the research, piezoelectric material is acted as an exciter to generate ultrasonic signals and a transducer is used to receive ultrasonic signals. In the process of signal processing, singular spectrum analysis (SSA) including phase reconstruction and principal component analysis is adopted to decompose the signal. Multiscale sample entropy (MSE) is employed to map the dynamic characteristics and regularity of the decomposed signals on multiple scales. The proposed strategy, named multiscale singular spectrum entropy analysis, refers to use MSE values of the new time series decomposed and reconstructed by SSA, to extract signal characteristics. Such a strategy can explore the underlying dynamical characteristics of a signal quantitatively in the reconstructed phase space. In our research work, SSA is employed to decompose the signals acquired by Lead Zirconate Titanate (PZT) to matrices, arranged from high to low singular values, and reconstruct the new time series (principal components) by diagonal averaging on determined matrices to characterize the essential dynamic characteristics of signals. MSE values of the principal components are used as damage index and adopted as input of genetic algorithm-based SVM to train a classifier to fulfill accurate monitoring of bolt joints. The theoretical derivation, application researches and comparison analysis can validate the effectiveness and superiority of the proposed approach in the field of bolt looseness monitoring. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is a common difficulty of human cardiovascular system and can be evaluated using cardiac rate variability. Multiscale Cross Sample Entropy (MCSEn) is used as a reference to quantify cardiac arrhythmia on the basis of complexity for double-interval series at multiple scales. This measure is failed to provide complexity with reduced scale factors for large data lengths. To hypothesize this measure for two series cardiac data by using coarse-grained process, Multiscale E-metric Cross Sample Entropy (MECSEn) has been proposed and is used to measure complexity between arrhythmia subjects, named atrial fibrillation (AF) and congestive heart failure (CHF) and healthy subjects at multiple scales. Besides short series data and undefined value, MECSEn has come up with a very new concept of banishing the use of a large number of scale factors for evaluating the complexity between two different interval series across multiple scales. It makes the proposed algorithm less time consumer. Both measures have found subjects derived from AF behave as white noise and subjects derived from CHF behave as pink noise. The t test validates MCSEn and the proposed algorithm, MECSEn by providing p < 0.00001. Moreover, MCSEn and MECSEn algorithms are compared with multiscale sample entropy algorithm (MSEn) which uses single cardiac series to evaluate complexity of healthy and arrhythmia subjects. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD. [ABSTRACT FROM AUTHOR]

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There is scarce evidence about early nutrition programming of dynamic aspects of glucose homeostasis. We analyzed the long-term effects of early nutrition on glycemic variability in healthy children. A total of 92 children participating in the COGNIS study were considered for this analysis, who were fed with: a standard infant formula (SF, n = 32), an experimental formula (EF, n = 32), supplemented with milk fat globule membrane (MFGM) components, long-chain polyunsaturated fatty acids (LC-PUFAs), and synbiotics, or were breastfed (BF, n = 28). At 6 years old, BF children had lower mean glucose levels and higher multiscale sample entropy (MSE) compared to those fed with SF. No differences in MSE were found between EF and BF groups. Normal and slow weight gain velocity during the first 6 months of life were associated with higher MSE at 6 years, suggesting an early programming effect against later metabolic disorders, thus similarly to what we observed in breastfed children. Conclusion: According to our results, BF and normal/slow weight gain velocity during early life seem to protect against glucose homeostasis dysregulation at 6 years old. EF shows functional similarities to BF regarding children's glucose variability. The detection of glucose dysregulation in healthy children would help to develop strategies to prevent the onset of metabolic disorders in adulthood. [ABSTRACT FROM AUTHOR]

In order to effectively mine the structural features in time series and simplify the complexity of time series analysis, equiprobable symbolization pattern entropy (EPSPE) is proposed in this paper. The original time series are implemented through symbolic processing according to an equal probability distribution. Then, the sliding window technique is used to obtain a finite number of different symbolic patterns, and the pattern pairs are determined by calculating the conversion between the symbolic patterns. Next, the conversion frequency between symbolized patterns is counted to calculate the probability of the pattern pairs, thus estimating the complexity measurement of complex signals. Finally, we conduct extensive experiments based on the Logistic system under different parameters and the natural wind field. The experimental results show our EPSPE of the Logistic system increases from 5 to 7.5 as the parameters increase, which makes the distinction of periodic and complex time series with varying degrees intuitive. Meanwhile, it can more concisely reflect the structural characteristics and interrelationships between time series from the natural wind field (8.8–10 for outdoor and 7.8–8.3 for indoor). In contrast, the results of several state-of-the-art schemes are irregular and cannot distinguish the complexity of periodic time series as well as accurately predict the spatial deployment relationship of nine 2D ultrasonic anemometers. [ABSTRACT FROM AUTHOR]

Monitoring the consciousness states of patients and ensuring the appropriate depth of anesthesia (DOA) is critical for the safe implementation of surgery. In this study, a high-density electroencephalogram (EEG) combined with blood drug concentration and behavioral response indicators was used to monitor propofol-induced sedation and evaluate the alterations in consciousness states. Microstate analysis, which can reflect the semi-stable state of the sub-second activation of the brain functional network, can be used to assess the brain’s consciousness states. In this research, the EEG microstate sequences were constructed to compare the characteristics of corresponding sequences. Compared with the baseline (BS) state, the microstate sequences in the moderate sedation (MD) state exhibited higher complexity indexes of the multiscale sample entropy. With respect to the transition probability (TP) of microstates, most microstates tended to be converted into microstate C in the BS state. In contrast, they tended to be converted into microstate F in the MD state. The significant difference between the expected TP and observed TP could lead to the conclusion that hidden layers were present when there were changes in the consciousness states. According to the hidden Markov model, the accuracy of distinguishing the BS and MD states was 80.16%. The characteristics of microstate sequence revealed the variations in the brain states caused by alterations in consciousness states during anesthesia from a new perspective and presented a new idea for monitoring the DOA.

Accurate underwater target detection and recognition in complex marine environments has always been a challenge. There is a lot of information in underwater target radiation noise that is important for underwater target recognition. However, the traditional underwater target radiation noise process is inefficient and inaccurate, severely limiting underwater target recognition. This paper proposed a new method for underwater target recognition based on compressed sensing multiscale entropy. For starters, compressing a signal improves its signal-to-noise ratio and broadens its linear spectrum. The multiscale sample entropy for the signal is then calculated after it has been denoised, and the most separated sample entropy is chosen by comparing the different scales of sample entropy to achieve effective underwater target radiation noise recognition. The experimental results show that the feature extraction method proposed in the paper can classify underwater target radiation noise quickly and effectively, improving recognition efficiency.

Timber has been commonly used in the field of civil engineering, and the health condition of timber is of great significance for the whole structure in practical scenarios. However, due to mechanical load and environmental impact, timber-based constructions are vulnerable to termite attack, microbial corrosion and fractures within their service lives. Thus, the damage monitoring of timber structures is very challenging under real situations. This paper presents an intelligent timber damage monitoring approach using Lead Zirconate Titanate (PZT)-enabled active sensing and intrinsic multiscale entropy analysis. The proposed approach adopts PZT-enabled active sensing to collect the signals depicting dynamic characteristics of the timber structure. The proposed intrinsic multiscale entropy analysis utilizes variational mode decomposition (VMD) to deal with the collected response signals. Decomposition of the response signals into a set of band-limited intrinsic mode functions (BLIMFs) denoting nonlinear and nonstationary characteristics. Then multiscale sample entropy (MSE) is employed to extract quantitative features, which are adopted as health condition indicators of timber structures. Finally, the convolutional neural network (CNN) fulfills the intelligent timber damage monitoring by using the quantitative features as the effective input. The research findings reveal the efficacy and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Ischemic stroke often leads to cognitive dysfunction, which delays the recovery process of patients. However, its pathogenesis is not yet clear. In this study, the cerebral ischemia-reperfusion model was built as the experimental object, and the hippocampal dentate gyrus (DG) was the target brain area. TTC staining was used to evaluate the degree of cerebral infarction, and nerve cell membrane potentials and local field potentials (LFPs) signals were collected to explore the mechanism of cognitive impairment in ischemia-reperfusion mice. The results showed that the infarcted area on the right side of the brain of the mice in the model group was white. The resting membrane potential, the number of action potential discharges, the post-hyperpolarization potential and the maximum ascending slope of the hippocampal DG nerve cells in the model mice were significantly lower than those in the control group ( P < 0.01); the peak time, half-wave width, threshold and maximum descending slope of the action potential were significantly higher than those in the control group ( P < 0.01). The time-frequency energy values of LFPs signals in the θ and γ bands of mice in the ischemia and reperfusion groups were significantly reduced ( P < 0.01), and the time-frequency energy values in the reperfusion group were increased compared with the ischemia group ( P < 0.01). The signal complexity of LFPs in the ischemia and reperfusion group was significantly reduced ( P < 0.05), and the signal complexity in the reperfusion group was increased compared with the ischemia group ( P < 0.05). In summary, cerebral ischemia-reperfusion reduced the excitability of nerve cells in the DG area of the mouse hippocampus; cerebral ischemia reduced the discharge activity and signal complexity of nerve cells, and the electrophysiological indicators recovered after reperfusion, but it failed to reach the healthy state during the experiment period.

Monitoring the consciousness states of patients and ensuring the appropriate depth of anesthesia (DOA) is critical for the safe implementation of surgery. In this study, a high-density electroencephalogram (EEG) combined with blood drug concentration and behavioral response indicators was used to monitor propofol-induced sedation and evaluate the alterations in consciousness states. Microstate analysis, which can reflect the semi-stable state of the sub-second activation of the brain functional network, can be used to assess the brain’s consciousness states. In this research, the EEG microstate sequences were constructed to compare the characteristics of corresponding sequences. Compared with the baseline (BS) state, the microstate sequences in the moderate sedation (MD) state exhibited higher complexity indexes of the multiscale sample entropy. With respect to the transition probability (TP) of microstates, most microstates tended to be converted into microstate C in the BS state. In contrast, they tended to be converted into microstate F in the MD state. The significant difference between the expected TP and observed TP could lead to the conclusion that hidden layers were present when there were changes in the consciousness states. According to the hidden Markov model, the accuracy of distinguishing the BS and MD states was 80.16%. The characteristics of microstate sequence revealed the variations in the brain states caused by alterations in consciousness states during anesthesia from a new perspective and presented a new idea for monitoring the DOA. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson’s disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.

Precipitation is a manifestation of many interacting complex processes. How to grasp its temporal pattern that would reveal underlain dominant contributing factors is the key objective of the study. For this, we explored the application of multiscale sample entropy (MSE) in describing the long-term daily precipitation. Sample entropy (SE) adds similarity measure over the conventional information entropy, and it has been used in quantifying changing complexity in chaotic dynamic systems. With the further incorporation of multiscale consideration, the MSE analysis gives the trend of SE changes with scale, and provides a rich description of participating factors. The daily precipitation time series studied were taken from 665 weather stations across China that have been recorded for about 50–61 years. The SE estimates are a function of the length of time series (n), the dimension of similarity (m), and the match threshold (r). These parameters are problem-dependent, and through simulation, this study has determined that m = 2, r = 0.15, and n ≈ 20,000 would be appropriate for estimating SE up to the 30-day scale. Three general patterns of MSE for precipitation time series are identified: (1) Pattern A, SE increases with scale; (2) Pattern B, SE increases then decreases and followed by increase; and (3) Pattern C, SE increases then decreases. The MSE is found capable of detecting differences in characteristics among precipitation time series. Matching MSE thus could serve as a metric to evaluate the adequacy of simulated precipitation time series. Using this metric, we have shown that to embody seasonal changes one needs to use different monthly two-parameter gamma distribution functions in generating simulated precipitation time series. Moreover, for dry seasons, one also needs to consider interannual fluctuations: it is inadequate to use just one single function for simulating multi-year precipitation data. Finally, for the study region, MSE patterns show coherence over the distance in that stations that are close, which range from 40 to 80 km, exhibit similar MSE trends. The MSE patterns obtained are also found to be reflective of the regional precipitation patterns—this has important implications on water resources management.

There is scarce evidence about early nutrition programming of dynamic aspects of glucose homeostasis. We analyzed the long-term effects of early nutrition on glycemic variability in healthy children. A total of 92 children participating in the COGNIS study were considered for this analysis, who were fed with: a standard infant formula (SF, n = 32), an experimental formula (EF, n = 32), supplemented with milk fat globule membrane (MFGM) components, long-chain polyunsaturated fatty acids (LC-PUFAs), and synbiotics, or were breastfed (BF, n = 28). At 6 years old, BF children had lower mean glucose levels and higher multiscale sample entropy (MSE) compared to those fed with SF. No differences in MSE were found between EF and BF groups. Normal and slow weight gain velocity during the first 6 months of life were associated with higher MSE at 6 years, suggesting an early programming effect against later metabolic disorders, thus similarly to what we observed in breastfed children. Conclusion: According to our results, BF and normal/slow weight gain velocity during early life seem to protect against glucose homeostasis dysregulation at 6 years old. EF shows functional similarities to BF regarding children’s glucose variability. The detection of glucose dysregulation in healthy children would help to develop strategies to prevent the onset of metabolic disorders in adulthood.

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

Timber has been commonly used in the field of civil engineering, and the health condition of timber is of great significance for the whole structure in practical scenarios. However, due to mechanical load and environmental impact, timber-based constructions are vulnerable to termite attack, microbial corrosion and fractures within their service lives. Thus, the damage monitoring of timber structures is very challenging under real situations. This paper presents an intelligent timber damage monitoring approach using Lead Zirconate Titanate (PZT)-enabled active sensing and intrinsic multiscale entropy analysis. The proposed approach adopts PZT-enabled active sensing to collect the signals depicting dynamic characteristics of the timber structure. The proposed intrinsic multiscale entropy analysis utilizes variational mode decomposition (VMD) to deal with the collected response signals. Decomposition of the response signals into a set of band-limited intrinsic mode functions (BLIMFs) denoting nonlinear and nonstationary characteristics. Then multiscale sample entropy (MSE) is employed to extract quantitative features, which are adopted as health condition indicators of timber structures. Finally, the convolutional neural network (CNN) fulfills the intelligent timber damage monitoring by using the quantitative features as the effective input. The research findings reveal the efficacy and superiority of the proposed method.

Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.

Objective: This study was undertaken to explore multimethod neurocognitive screening tools to aid in detection of older adults who may be at heightened risk of pathological cognitive decline (preclinical dementia). In so doing, this study advances the theoretical conceptualization of neurocognitive adaptability in the context of aging and dementia. Method: This article reports original data from the baseline measurement occasion of a longitudinal study of healthy, community-dwelling older adults from the Victoria, British Columbia region. Participants were diagnosed as normal, subtle decline, or mild cognitive impairment according to actuarial neuropsychological criteria (adjusted for age only or adjusted for age and premorbid IQ). Diagnostic classification was employed to illustrate group differences in a novel metric of multi-timescale neural adaptability derived from 4-min of resting-state electroencephalographic data collected from each participant (immediately following their neuropsychological evaluation). Results: Prior findings were replicated; adjusting raw neuropsychological test scores for individual differences in estimated premorbid IQ appeared to increase the sensitivity of standardized clinical tasks to subtle cognitive impairment. Moreover, and consistent with prior neuroscientific research, timescale-specific (i.e. at ∼12-20 ms timescales) differences in resting-state neural adaptability appeared to characterize groups who differed in terms of neuropsycholgoical diagnostic classification. Conclusions: Recently proposed actuarial neuropsychological criteria for subtle cognitive decline identify older adults who show timescale-specific changes in resting brain function that may signal the onset of preclinical dementia. The subtle decline stage may represent a critical inflection point—partial loss of neurocognitive adaptability—on a pathological aging trajectory. These findings illustrate areas of potential future development in neurocognitive health care. [ABSTRACT FROM AUTHOR]

Despite extensive recent interest in mild cognitive impairment (MCI) and Alzheimer's disease (AD), few studies have examined the lateralization of their brain functions. This study describes the multiscale lateralized brain entropy (multi-LBE) algorithm and investigates the lateralization of brain functional complexity for MCI and AD. The lateralized sample entropy (LSE) and lateralized cross-sample entropy (LCSE) are calculated over multiple time scales and used as lateralization indices of brain functional complexity to explore the progression of MCI-AD (from healthy control to early MCI, late MCI and AD). Our experimental results indicate that patients with AD have the weakest lateralization of functional complexity in most brain regions. The left hemisphere exhibits a trend to rightward laterality over an increasing number of time scales during the MCI-AD progression. Correlation analysis of LBEs demonstrates positive correlations among three patient groups, while negative correlations are found between healthy control group and each patient group. Furthermore, significance tests are performed on the whole brain regions of the four groups at multiple time scales, and the LBEs of several time scales are selected as features. The classification of the four groups using multi-LBEs achieves an accuracy of up to 97.46% by multiscale LBEs, indicating that the LBE is a possible indicator of different states during MCI-AD progression. Our findings provide new insights into the lateralization of brain functional complexity and highlight the potential for using multi-LBEs as biomarkers for the MCI-AD. • We proposed the multiscale Lateralized Brain Entropy (Multi-LBE) algorithm. • Study the lateralization of brain functional complexity of MCI-AD progression • AD has the weakest lateralization of functional complexity in most brain regions. • The left hemisphere showed a trend to rightward laterality during MCI-AD progression. • The classification of the four groups was accurate up to 97.46% by multiscale LBEs. [ABSTRACT FROM AUTHOR]

The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson’s disease (PD), HC vs. Huntington’s disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers.

Multivariate multiscale sample entropy (MMSE) is a robust method to detect the complexity of multivariate system. It is evaluated for a certain value of tolerance parameter r which is mainly calculated from common acknowledged range. This kind of selection of r is not suitable for short-term time series and may lead to the unreliable detection. To reduce the impact of limited range of r, we apply cumulative histogram method to estimate the range of r. It is data-driven and needs no parameters. Moreover, we use secondary statistics, AvgMMSE and SDMMSE rather than the single value of MMSE to detect the complexity of signals and differentiate them. Several time series, either generated from chaotic or stochastic systems, are analyzed to demonstrate the approach. The core achievement of this experiment is the stability and classification for short-term time series. Then we apply this method to financial time series. Empirical results show that the proposed method is vigorous enough to classify different stock indices over different periods. [ABSTRACT FROM AUTHOR]

How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiologicalmechanismof chronic pain. In this study, complexity featureswithmultiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDMpatients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations betweenMSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-termmenstrual pain. These findings suggest thatMSE is an importantmeasure of brain complexity and is potentially applicable to future diagnosis of chronic pain. [ABSTRACT FROM AUTHOR]

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell’s circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51−100Hz) of EEG signals rather than low frequency oscillations (0.3−49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

This paper proposes an intelligent diagnosis method for gearbox using local mean decomposition and discrete hidden Markov models, including local mean decomposition, the energy difference spectrum of singular value, multiscale sample entropy, and the discrete hidden Markov model. How to extract feature information effectively and identify the fault type is key to making a diagnosis in the presence of strong noise. Combined with the Kurtosis criterion and correlation coefficient, the product function that contains the main characteristic frequency is filtered out by local mean decomposition. Next, the filtered local mean decompositions are used to construct the Hankel matrix and complete singular value decomposition. The denoised and reconstructed signals are achieved by an energy difference spectrum of singular value. Furthermore, the feature information after denoising is extracted by multiscale sample entropy. After combining the discrete hidden Markov models, the mechanical condition is identified. Practical examples of diagnoses for four gear types used in the gearbox can accurately identify the gear types, and the recognition rates of the various types are above 92%. The experiments shown here verify the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

A new financial agent-based time series model is developed and investigated by multiscale-continuum percolation system, which can be viewed as an extended version of continuum percolation system. In this financial model, for different parameters of proportion and density, two Poisson point processes (where the radii of points represent the ability of receiving or transmitting information among investors) are applied to model a random stock price process, in an attempt to investigate the fluctuation dynamics of the financial market. To validate its effectiveness and rationality, we compare the statistical behaviors and the multifractal behaviors of the simulated data derived from the proposed model with those of the real stock markets. Further, the multiscale sample entropy analysis is employed to study the complexity of the returns, and the cross-sample entropy analysis is applied to measure the degree of asynchrony of return autocorrelation time series. The empirical results indicate that the proposed financial model can simulate and reproduce some significant characteristics of the real stock markets to a certain extent. [ABSTRACT FROM AUTHOR]

A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19-70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions (). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings. [ABSTRACT FROM AUTHOR]

We study the dynamical properties of avalanche activities in the Korean Treasury Bond (KTB) futures price and the S&P 500 stock index. We apply the detrended fluctuation analysis, multiscale sample entropy and wavelet coefficient correlation to them, which revealed the scale-free dynamics of the bursting time series, avalanche size, and laminar time. We found that the laminar time and the avalanche size are anti-correlated in a short scale but in a large scale strongly correlated in KTB503, and are strongly correlated over all scales in S&P 500. [ABSTRACT FROM AUTHOR]

Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer's disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a "transitional change" between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson's disease (PD), HC vs. Huntington's disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers. [ABSTRACT FROM AUTHOR]

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals. [ABSTRACT FROM AUTHOR]

Currently, the rapid development of continuous glucose monitoring (CGM) device brings new insights into the treatment of diabetic patients including those during pregnancy. Complexity and fractality have recently under fast development for extracting information embodied in glucose dynamics measured using CGM. Although scientists have investigated the difference of complexity in glucose dynamics between diabetes and non-diabetes in order to discover better approaches for diabetes care, no one has analyzed the complexity and fractality of glucose dynamics during the process of adopting CGM to successfully treat pregnant women with type 2 diabetes. Thus, we analyzed the complexity and fractality using power spectral density (PSD), multi-scale sample entropy (MSE) and multifractal detrended fluctuation analysis (MF-DFA) in a clinical case. Our results show that (i) there exists multifractal behavior in blood glucose dynamics; (ii) the alpha stable distribution fits to the glucose increment data better than the Gaussian distribution; and (iii) the "global" complexity indicated by multiscale entropy, spectrum exponent and Hurst exponent increase and the "local" complexity indicated by multifractal spectrum decrease after the successful therapy. Our results offer findings that may bring value to health care providers for managing glucose levels of pregnant women with type 2 diabetes as well as provide scientists a reference on applying complexity and fractality in the clinical practice of treating diabetes., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

For musicians, performing in front of an audience can cause considerable apprehension; indeed, performance anxiety is felt throughout the profession, with wide ranging symptoms arising irrespective of age, skill level and amount of practice. A key indicator of stress is frequency-specific fluctuations in the dynamics of heart rate known as heart rate variability (HRV). Recent developments in sensor technology have made possible the measurement of physiological parameters reflecting HRV non-invasively and outside of the laboratory, opening research avenues for real-time performer feedback to help improve stress management. However, the study of stress using standard algorithms has led to conflicting and inconsistent results. Here, we present an innovative and rigorous approach which combines: (i) a controlled and repeatable experiment in which the physiological response of an expert musician was evaluated in a low-stress performance and a high-stress recital for an audience of 400 people, (ii) a piece of music with varying physical and cognitive demands, and (iii) dynamic stress level assessment with standard and state-of-the-art HRV analysis algorithms such as those within the domain of complexity science which account for higher order stress signatures. We show that this offers new scope for interpreting the autonomic nervous system response to stress in real-world scenarios, with the evolution of stress levels being consistent with the difficulty of the music being played, superimposed on the stress caused by performing in front of an audience. For an emerging class of algorithms that can analyse HRV independent of absolute data scaling, it is shown that complexity science performs a more accurate assessment of average stress levels, thus providing greater insight into the degree of physiological change experienced by musicians when performing in public.