Multiscale Entropy Analysis Research Articles

Multiscale entropy analysis of astronomical time series

Context.The multiscale entropy assesses the complexity of a signal across different timescales. It originates from the biomedical domain and was recently successfully used to characterize light curves as part of a supervised machine learning framework to classify stellar variability.Aims.We aim to explore the behavior of the multiscale entropy in detail by studying its algorithmic properties in a stellar variability context and by linking it with traditional astronomical time series analysis methods and metrics such as the Lomb-Scargle periodogram. We subsequently use the multiscale entropy as the basis for an interpretable clustering framework that can distinguish hybrid pulsators with bothp- and g-modes from stars with onlyp-mode pulsations, such asδScuti (δSct) stars, or from stars with onlyg-mode pulsations, such asγDoradus (γDor) stars.Methods.We calculate the multiscale entropy for a set ofKeplerlight curves and simulated sine waves. We link the multiscale entropy to the type of stellar variability and to the frequency content of a signal through a correlation analysis and a set of simulations. The dimensionality of the multiscale entropy is reduced to two dimensions and is subsequently used as input to the HDBSCAN density-based clustering algorithm in order to find the hybrid pulsators within sets ofδSct andγDor stars that were observed byKepler.Results.We find that the multiscale entropy is a powerful tool for capturing variability patterns in stellar light curves. The multiscale entropy provides insights into the pulsation structure of a star and reveals how short- and long-term variability interact with each other based on time-domain information only. We also show that the multiscale entropy is correlated to the frequency content of a stellar signal and in particular to the near-core rotation rates ofg-mode pulsators. We find that our new clustering framework can successfully identify the hybrid pulsators with bothp- andg-modes in sets ofδSct andγDor stars, respectively. The benefit of our clustering framework is that it is unsupervised. It therefore does not require previously labeled data and hence is not biased by previous knowledge.

Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions.

Recent studies suggest that the maintenance of cognitive function in the later life of older people is an essential factor contributing to mental wellbeing and physical health. Particularly, the risk of depression, sleep disorders, and Alzheimer's disease significantly increases in patients with mild cognitive impairment. To develop early treatment and prevention strategies for cognitive decline, it is necessary to individually identify the current state of cognitive function since the progression of cognitive decline varies among individuals. Therefore, the development of biomarkers that allow easier measurement of cognitive function in older individuals is relevant for hyperaged societies. One of the methods used to estimate cognitive function focuses on the temporal complexity of electroencephalography (EEG) signals. The characteristics of temporal complexity depend on the time scale, which reflects the range of neuron functional interactions. To capture the dynamics, composed of multiple time scales, multiscale entropy (MSE) analysis is effective for comprehensively assessing the neural activity underlying cognitive function in the brain. Thus, we hypothesized that EEG complexity analysis could serve to assess a wide range of cognitive functions in older adults. To validate our hypothesis, we divided older participants into two groups based on their cognitive function test scores: a high cognitive function group and a low cognitive function group, and applied MSE analysis to the measured EEG data of all participants. The results of the repeated-measures analysis of covariance using age and sex as a covariate in the MSE profile showed a significant difference between the high and low cognitive function groups (F = 10.18, p = 0.003) and the interaction of the group × electrodes (F = 3.93, p = 0.002). Subsequently, the results of the post-hoc t-test showed high complexity on a slower time scale in the frontal, parietal, and temporal lobes in the high cognitive function group. This high complexity on a slow time scale reflects the activation of long-distance neural interactions among various brain regions to achieve high cognitive functions. This finding could facilitate the development of a tool for diagnosis of cognitive decline in older individuals.

Intelligent Timber Damage Monitoring Using PZT-Enabled Active Sensing and Intrinsic Multiscale Entropy Analysis

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.

Age-related difference in muscle metabolism patterns during upper limb's encircling exercise: a near-infrared spectroscopy study.

Aging is usually accompanied by decrease in limb motor function and change in muscle metabolism patterns. However, few studies have investigated the aging effect on muscle hemodynamics of the upper extremity. This study aims to explore the aging effect on muscle metabolism patterns during upper limb's exercise. Twelve middle-aged and elderly subjects and 12 young subjects were recruited, and muscle oxygenation signals from these subjects' biceps brachii muscles were collected during active and passive upper limb's encircling exercise with near-infrared spectroscopy (NIRS). The old group showed stronger muscle hemodynamic metabolism than the young group. The multiscale fuzzy approximate entropy and multiscale transfer entropy analyses indicated higher complexity and stronger interlimb coupling of the muscle oxygenation signals for the old group. Based on the selected muscle metabolism features, the constructed support vector machine model showed a high accuracy rate for classifying the two groups of subjects: 91.6% for the passive mode and 87.5% for the active mode. Our results proved the specific muscle metabolism patterns in the upper limb's exercise for old subjects, promoting the understanding of the aging effect on muscle hemodynamics.

Modified multiscale transfer entropy analysis of intra- and inter-couplings of cardio-respiratory systems during meditation

The complex nonstationary interconnection between cardiovascular and respiratory systems ensures the normal metabolism of the human body. However, the dynamic and multiscale characteristics of the interconnection are not fully understood, especially the alterations of cardiac sympathetic-parasympathetic and cardio-respiratory couplings. In this study, modified multiscale transfer entropy (MMTE) was proposed and applied to investigate the intra- and inter-couplings of cardio-respiratory systems. In the experiment, 18 healthy young subjects were recruited to finish the meditation task, and electrocardiogram (ECG) and respiratory signals were recorded in the pre-, during-, and post-task phases. A simulation study has been conducted to test the reliability of MMTE by cardio-respiratory and sympathetic-parasympathetic dynamics models. For the two physiological simulation models, MMTE on scales 10–20 possessed the ability in noise abatement and superior reliability of quantifying coupling strength to that on other scales. Experimental results showed significantly increased MMTE values from the respiratory series to the RR interval series (the time intervals between consecutive R waves of the ECG) over multiple time scales compared with the opposite direction. The same is true for the MMTE values from low frequency (LF) to high frequency (HF) components of the RR interval series. Moreover, the MMTE values between respiratory and RR interval series and between LF and HF series in the during-task phase were significantly smaller than those in other phases. These findings expand the existing mechanism of intra- and inter-couplings of cardio-respiratory systems induced by meditation. Further research is needed to validate and promote the clinical applications.

A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction

Accurately predicting precious metal prices is of extreme significance as they possess an essential position in both financial and industrial fields. To achieve higher prediction accuracy, in this paper, a modified ensemble empirical mode decomposition (MEEMD) method integrated with long short-term memory neural network (LSTM) is used for precious metal price prediction. The multi-scale permutation entropy (MPE) analysis demonstrated that MEEMD has better decomposition effect than ensemble empirical mode decomposition (EEMD). Then input each intrinsic mode function (IMF) obtained by MEEMD into LSTM for prediction. Finally add each IMF forecasting value to get the final prediction results. Compared with traditional multilayer perceptron neural network (MLP), support vector regression (SVR) and a combination forecasting model super learner (SL), MEEMD-LSTM improves the prediction performance in one-step ahead prediction and multi-step ahead prediction. The multi-horizon model confidence set (MCS) test are adopted to comprehensively and statistically testify the best prediction performance of MEEMD-LSTM. Furthermore, this research indicates that the model still shows better prediction accuracy either under different proportions of the training set to the test set or in different periods of business cycle. This competitive precious metal price forecasting model is a promising technique for government agencies, investors and related enterprises.

Very Short-Term Photoplethysmography-Based Heart Rate Variability for Continuous Autoregulation Assessment

Background: Heart rate variability (HRV) has been widely applied for disease diagnosis. However, the 5 min signal length for HRV analysis is needed. Method: A signal processing procedure for very short-term photoplethysmography (PPG) signal for fever detection and autoregulation assessment was proposed. The Time-Shift Multiscale Entropy Analysis (TSME) was applied to instantaneous pulse rate time series (iPR) and normalized by the cumulative distribution function (CDF) of all scales to calculate novel indices. A total of 33 subjects were recruited for the study. Fifteen participants whose body temperatures were higher than 37.9 °C were served as the fever group. Others were served as the non-fever group. The total 15 s PPG signal with 200 sampling rates was used for iPR calculation. Result: The CDF value of entropy on the scale k = 19 (CDF(E(k = 19))) of iPR had the lowest p-value calculated by the Weltch t-test between two groups (p < 0.001). The Spearman correlation r between CDF(E(k = 19)) and body temperature is −0.757, 0.287, and −0.830 in all subjects, the non-fever group and the Fever group, respectively. The area under the curve, calculated from the receiver operating characteristic of CDF(E(k = 19)) of iPR is 0.915. Conclusion: The entropy of iPR is useful for detecting fever. Moreover, a short-term PPG signal is suitable to develop real-time applications, and multiscale entropy provides different scales of information for daily healthcare.

91-OR: Association between Complexity of Glucose Time Series and All-Cause Mortality in Patients with Type 2 Diabetes Mellitus: A Prospective Cohort study

Background: Continuous glucose monitoring (CGM) can comprehensively reflect the dynamic changes of blood glucose varying with time. CGM data with more patterns of change represents glucose time series with higher complexity. However, the relationship between complexity of glucose dynamics and clinical outcomes of diabetes remains unknown. Objective: To investigate the association between complexity of glucose time series, quantified by multiscale entropy (MSE) analysis, and all-cause mortality in patients with type 2 diabetes mellitus (T2DM) in a prospective cohort study. Methods: Data of 6,001 adult inpatients with T2DM included from January 2005 to December 2015 in a single center was analyzed. Complexity of glucose index (CGI) of glucose time series from CGM was measured by MSE analysis at baseline. Participants were stratified by the tertiles of CGI: < 2.15,2.15∼2.99, and ≥ 3.00. Cox proportional hazards regression models were used to estimate the association between different levels of CGI and the risks of all-cause mortality. Results: During a median follow-up of 6.8 years, 799 deaths were identified. Multivariate Cox regression analysis revealed that the increase of CGI was associated with a lower risk of all-cause mortality (P for trend = 0.021) . The multivariable-adjusted HRs for all-cause mortality at different CGI levels [< 2.15 (reference group) , 2.15∼2.99, and ≥ 3.00] were 1.00, 0.49 (95%CI 0.30-0.79) , and 0.56 (95%CI 0.36-0.87) in patients with HbA1c < 7.0% (P for trend = 0.015) , while the association was nonsignificant in those with HbA1c ≥ 7.0%. And a significant interaction between HbA1c and CGI (P for interaction = 0.032) was noted in the study. Conclusion: Lower CGI is associated with an increased risk of all-cause mortality in T2DM achieving HbA1c target, which suggests that the improvement of CGI may further reduce the risk of long-term adverse outcomes in T2DM at an early stage or with good glycemic control. Disclosure J.Cai: None. J.Lu: None. Y.Shen: None. W.Lu: None. W.Zhu: None. J.Zhou: None. Funding The Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (20161430)

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection.

Recently, tools developed for detecting human activities have been quite prominent in contributing to health issue prevention and long-term healthcare. For this occasion, the current study aimed to evaluate the performance of eye-movement complexity features (from multi-scale entropy analysis) compared to eye-movement conventional features (from basic statistical measurements) on detecting daily computer activities, comprising reading an English scientific paper, watching an English movie-trailer video, and typing English sentences. A total of 150 students participated in these computer activities. The participants’ eye movements were captured using a desktop eye-tracker (GP3 HD Gazepoint™ Canada) while performing the experimental tasks. The collected eye-movement data were then processed to obtain 56 conventional and 550 complexity features of eye movement. A statistic test, analysis of variance (ANOVA), was performed to screen these features, which resulted in 45 conventional and 379 complexity features. These eye-movement features with four combinations were used to build 12 AI models using Support Vector Machine, Decision Tree, and Random Forest (RF). The comparisons of the models showed the superiority of complexity features (85.34% of accuracy) compared to conventional features (66.98% of accuracy). Furthermore, screening eye-movement features using ANOVA enhances 2.29% of recognition accuracy. This study proves the superiority of eye-movement complexity features.

Multiscale entropy analysis of retinal signals reveals reduced complexity in a mouse model of Alzheimer’s disease

Alzheimer’s disease (AD) is one of the most significant health challenges of our time, affecting a growing number of the elderly population. In recent years, the retina has received increased attention as a candidate for AD biomarkers since it appears to manifest the pathological signatures of the disease. Therefore, its electrical activity may hint at AD-related physiological changes. However, it is unclear how AD affects retinal electrophysiology and what tools are more appropriate to detect these possible changes. In this study, we used entropy tools to estimate the complexity of the dynamics of healthy and diseased retinas at different ages. We recorded microelectroretinogram responses to visual stimuli of different nature from retinas of young and adult, wild-type and 5xFAD—an animal model of AD—mice. To estimate the complexity of signals, we used the multiscale entropy approach, which calculates the entropy at several time scales using a coarse graining procedure. We found that young retinas had more complex responses to different visual stimuli. Further, the responses of young, wild-type retinas to natural-like stimuli exhibited significantly higher complexity than young, 5xFAD retinas. Our findings support a theory of complexity-loss with aging and disease and can have significant implications for early AD diagnosis.

Lead Zirconate Titanate-based bolt looseness monitoring using multiscale singular spectrum entropy analysis and genetic algorithm-based support vector machine

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.

Linking the sampling frequency with multiscale entropy to classify mitoBK patch-clamp data

We analyze the activity of large-conductance voltage- and Ca2+-activated potassium channels located in the inner mitochondrial membrane (mitoBK) from human dermal fibroblast cells. The ion current activity registered via the patch-clamp technique was taken into consideration. At the preliminary stage, we performed an in-depth analysis of the signal power spectrum to find an optimal sampling frequency and study the impact of different sampling on changes in the information hidden in the signal. We found the optimal 10 kHz sampling frequency for the fibroblast’s mitoBK currents sequences. Interestingly, as the signal sampling rate increases, we can observe a decrease in entropy values. The application of Multiscale Entropy analysis enabled a practical classification of single-channel current traces at various membrane potentials. Using the machine learning techniques such as K-Nearest Neighbors and Support Vector Machine, optimized by the Stochastic Gradient Descent algorithm with Sample Entropy values as inputs, allowed us to assess the more outstanding accuracy scores for the chosen classifiers at membrane the hyperpolarization than at its depolarization.

Altered Brain Activity in Depression of Parkinson's Disease: A Meta-Analysis and Validation Study.

BackgroundThe pathophysiology of depression in Parkinson’s disease (PD) is not fully understood. Studies based upon functional MRI (fMRI) showed the alterations in the blood-oxygen-level-dependent (BOLD) fluctuations in multiple brain regions pertaining to depression in PD. However, large variance was observed across previous studies. Therefore, we conducted a meta-analysis to quantitatively evaluate the results in previous publications and completed an independent regions-of-interests (ROIs)-based analysis using our own data to validate the results of the meta-analysis.MethodsWe searched PubMed, Embase, and Web of Science to identify fMRI studies in PD patients with depression. Using signed differential mapping (SDM) method, we performed a voxel-based meta-analysis. Then, a validation study by using multiscale entropy (MSE) in 28 PD patients with depression and 25 PD patients without depression was conducted. The fMRI scan was completed in anti-depression-medication-off state. The ROIs of the MSE analysis were the regions identified by the meta-analysis.ResultsA total of 126 PD patients with depression and 153 PD patients without depression were included in meta-analysis. It was observed that the resting-state activities within the posterior cingulate gyrus, supplementary motor area (SMA), and cerebellum were altered in depressed patients. Then, in the validation study, these regions were used as ROIs. PD patients with depression had significantly lower MSE of the BOLD fluctuations in these regions (posterior cingulate gyrus: F = 0.856, p = 0.049; SMA: F = 0.914, p = 0.039; cerebellum: F = 0.227, p = 0.043).ConclusionOur study revealed that the altered BOLD activity in cingulate, SMA, and cerebellum of the brain were pertaining to depression in PD.

Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing

Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The condition of bolt joints has a significant impact on the safe and reliable operation of the whole equipment. The failure of bolt joints monitoring leads to severe accidents or even casualties. This paper proposes a novel bolt joints monitoring method using multivariate intrinsic multiscale entropy (MIME) analysis and Lorentz signal-enhanced piezoelectric active sensing. Lorentz signal is used as excitation signal in piezoelectric active sensing to expose nonlinear dynamical characteristics of the bolt joints. Multivariate variational mode decomposition (MVMD) is employed to decompose multiple components of the collected Lorentz signal into multivariate band-limited intrinsic mode functions (BLIMFs). Afterward, improved multiscale sample entropy (IMSE) values of each channel’s BLIMFs are computed to measure its irregularity and complexity. IMSE values are taken as quantitative features, reflecting dynamical characteristics of bolt joints. Further, the constructed 3-layer feature matrices are adopted as the input of the convolutional neural network (CNN) to achieve accurate bolt joint monitoring. The multiple M1 bolt joints are used during the experiment to verify the effectiveness and superiority of the proposed approach. The results demonstrate the proposed novel approach is promising in bolt joints monitoring.

Climate Variation within the Range of Longleaf Pine Forests during the Past Century

Longleaf pine (Pinus palustris Mill.) forests are an important ecosystem in the southeastern United States, with high economic and ecological value. It is necessary to study the climate variation within its range in order to understand the effects of climate change on longleaf pine forests. In this study, past climate data at three sites within the longleaf pine range were used to detect climate variation. The results indicated no dramatic change in solar radiation at the three sites. There were high variations in annual air temperature at the three sites. The trend of annual air temperature change depended on the time scale and start/end time. The annual air temperature generally increased from the 1960s at three sites. However, from 1901 to 2020, the trend of increasing annual air temperature was not consistent. The annual precipitation and the standardized precipitation-evapotranspiration index were relatively stable, with variation at the three sites. The regimes of annual and monthly air temperature and precipitation were not shifted based on the analysis of multiscale entropy. The climate niche of longleaf pine forests based on long-term climate data was broader than previously found. These results may be helpful to understand the interactions of the atmosphere and growth of longleaf pine forest and develop relevant management strategies.

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis.

Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals.

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography.

With the aging process, brain functions, such as attention, memory, and cognitive functions, degrade over time. In a super-aging society, the alteration of neural activity owing to aging is considered crucial for interventions for the prevention of brain dysfunction. The complexity of temporal neural fluctuations with temporal scale dependency plays an important role in optimal brain information processing, such as perception and thinking. Complexity analysis is a useful approach for detecting cortical alteration in healthy individuals, as well as in pathological conditions, such as senile psychiatric disorders, resulting in changes in neural activity interactions among a wide range of brain regions. Multi-fractal (MF) and multi-scale entropy (MSE) analyses are known methods for capturing the complexity of temporal scale dependency of neural activity in the brain. MF and MSE analyses exhibit high accuracy in detecting changes in neural activity and are superior with regard to complexity detection when compared with other methods. In addition to complex temporal fluctuations, functional connectivity reflects the integration of information of brain processes in each region, described as mutual interactions of neural activity among brain regions. Thus, we hypothesized that the complementary relationship between functional connectivity and complexity could improve the ability to detect the alteration of spatiotemporal patterns observed on electroencephalography (EEG) with respect to aging. To prove this hypothesis, this study investigated the relationship between the complexity of neural activity and functional connectivity in aging based on EEG findings. Concretely, MF and MSE analyses were performed to evaluate the temporal complexity profiles, and phase lag index analyses assessing the unique profile of functional connectivity were performed based on the EEGs conducted for young and older participants. Subsequently, these profiles were combined through machine learning. We found that the complementary relationship between complexity and functional connectivity improves the classification accuracy among aging participants. Thus, the outcome of this study could be beneficial in formulating interventions for the prevention of age-related brain dysfunction.

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment.

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p < 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p < 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p < 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p < 0.01).Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

Empirical mode decomposition-refined composite multiscale dispersion entropy analysis and its application to geophysical well log data

Oil and gas exploration activities often face great challenges due to the nonlinear behavior of the reservoir's physical properties, which is commonly defined as “heterogeneity”. Currently, well log data analysis techniques are a novel approach to unravel such nonlinear behavior, because well log data incorporates considerable geological information that determines reservoir property. However, the current complexity analysis techniques face two challenges: 1) the spatiotemporal multiscale nature of complex geological systems and 2) the superposition of the trends in the geophysical well log data on the analysis results. To fill the research gap, we propose an empirical mode decomposition-refined composite multiscale dispersion entropy analysis (EMD-RCMDEA) to eradicate trends and obtain the complexity results with spatiotemporal characteristics. The proposed method produces more accurate results, and its effectiveness, stability, and efficiency are also verified by the simulation signals and the gamma-ray (GR) well log signals. Compared to previous refined composite multiscale entropy analysis (RCMSEA), the EMD-RCMDEA enhances the stability by 69.3% and efficiency by 53.5%. Additionally, using the GR well log data for reservoirs, this method is also applied to explore the heterogeneity of strata with diverse depositional environments and different composite patterns and acquire the following results. 1) The EMD-RCMDEA values of the GR well log data are positively correlated with the heterogeneity of the strata. 2) The reservoir developed in a delta-front depositional environment has the strongest heterogeneity. 3) The heterogeneity of the composite patterns is much stronger than that of the single heterogeneity patterns. 4) Among the heterogeneities of the composite patterns, the pattern consisting of different facies is stronger than that for single facies. • A novel heterogeneity evaluation method is proposed based on EMD-RCMDEA. • New metrics for evaluating the stability of the multiscale entropy method are proposed. • The EMD-RCMDEA eliminates the drawback of multiscale entropy methods. • Trends superimposed in signals affect complexity analysis results. • The EMD-RCMDEA values of GR well log data positively correlate with reservoir heterogeneity.