Multiscale Sample Entropy Research Articles

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns.

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

Decreased frontal lobe complexity in left-behind children during joint attention: a fNIRS study with multivariable and multiscale sample entropy analysis.

Human brain development is shaped by experiences, especially during preschool, the critical period for cognitive and socioemotional development. This study employed the functional Near-Infrared Spectroscopy technique to explore the neural differences between left-behind children (LBC) and non-left-behind children (NLBC) on joint attention. Through collecting brain image data of 50 children (26 boys, aged 65.08 ± 6.28months) and conducting multivariable and multiscale sample entropy (MMSE) analysis, the present study found that: (i) LBC showed lower brain complexity than NLBC in right prefrontal cortex; (ii) all participants demonstrated higher brain complexity in responding to joint attention conditions, compared to initiating joint attention ones; (iii) their brain complexity during joint attention was negatively associated with their emotional abilities. The findings advance our understanding of early brain development in LBC by providing evidence for the neural process characteristics of joint attention. Implications for early intervention to promote their brain development are also addressed.

Distinguishing between deterministic oscillations and noise

AbstractTime-dependent dynamics is ubiquitous in the natural world and beyond. Effectively analysing its presence in data is essential to our ability to understand the systems from which it is recorded. However, the traditional framework for dynamics analysis is in terms of time-independent dynamical systems and long-term statistics, as opposed to the explicit tracking over time of time-localised dynamical behaviour. We review commonly used analysis techniques based on this traditional statistical framework—such as the autocorrelation function, power-spectral density, and multiscale sample entropy—and contrast to an alternative framework in terms of finite-time dynamics of networks of time-dependent cyclic processes. In time-independent systems, the net effect of a large number of individually intractable contributions may be considered as noise; we show that time-dependent oscillator systems with only a small number of contributions may appear noise-like when analysed according to the traditional framework using power-spectral density estimation. However, methods characteristic of the time-dependent finite-time-dynamics framework, such as the wavelet transform and wavelet bispectrum, are able to identify the determinism and provide crucial information about the analysed system. Finally, we compare these two frameworks for three sets of experimental data. We demonstrate that while techniques based on the traditional framework are unable to reliably detect and understand underlying time-dependent dynamics, the alternative framework identifies deterministic oscillations and interactions.

Multi-Scale Sample Entropy Analysis of the Turkish Stock Market Efficiency

This study evaluates the market efficiency of the market index and five main sector indices in the Turkish stock market: BIST 100 (XU100), BIST Industrials (XUSIN), BIST Services (XUHIZ), BIST Transportation (XULAS), BIST Financials (XUMAL), and BIST Technology (XUTEK) for the pre-and post-COVID-19 pandemic, covering the period from January 2017 to July 2022. Market efficiency is calculated using a multiscale entropy-based method for the scales of 1 to 20 business days. Entropy can yield a relative degree of efficiency, by contrast with previous methods that dealt with the efficiency question in all-or-nothing form. On a daily scale, during the pre-COVID-19 pandemic period, the XUHIZ, XU100 and XULAS indices exhibit the highest efficiency. However, in the post-COVID-19 pandemic period, the XUMAL and XU100 indices demonstrate the highest efficiency. The findings suggest that the efficiency of all indices has declined due to the COVID-19 pandemic, with the XULAS index showing the most significant decrease in informational efficiency. A general tendency of reduced informational efficiency levels is found as the time scale increases in both periods. Therefore, the indices are partially efficient for certain time scales, indicating that they are not fully efficient.

Improved multivariate multiscale sample entropy and its application in multi-channel data.

Entropy, as a nonlinear feature in information science, has drawn much attention for time series analysis. Entropy features have been used to measure the complexity behavior of time series. However, traditional entropy methods mainly focus on one-dimensional time series originating from single-channel transducers and are incapable of handling the multidimensional time series from multi-channel transducers. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although MMSE generalizes multiscale sample entropy and provides a new method for multidimensional data analysis, it lacks necessary theoretical support and has shortcomings, such as missing cross-channel correlation information and having biased estimation results. This paper proposes an improved multivariate multiscale sample entropy (IMMSE) algorithm to overcome these shortcomings. This paper highlights the existing shortcomings in MMSE under the generalized algorithm. The rationality of IMMSE is theoretically proven using probability theory. Simulations and real-world data analysis have shown that IMMSE is capable of effectively extracting cross-channel correlation information and demonstrating robustness in practical applications. Moreover, it provides theoretical support for generalizing single-channel entropy methods to multi-channel situations.

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease.

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.

Application of Multiscale Sample Entropy in Assessing Effects of Exercise Training on Skin Blood Flow Oscillations in People with Spinal Cord Injury

Spinal cord injury (SCI) causes a disruption of autonomic nervous regulation to the cardiovascular system, leading to various cardiovascular and microvascular diseases. Exercise training is an effective intervention for reducing risk for microvascular diseases in healthy people. However, the effectiveness of exercise training on improving microvascular function in people with SCI is largely unknown. The purpose of this study was to compare blood flow oscillations in people with spinal cord injury and different physical activity levels to determine if such a lifestyle might influence skin blood flow. A total of 37 participants were recruited for this study, including 12 athletes with SCI (ASCI), 9 participants with SCI and a sedentary lifestyle (SSCI), and 16 healthy able-bodied controls (AB). Sacral skin blood flow (SBF) in response to local heating at 42 °C for 50 min was measured using laser Doppler flowmetry. The degree of the regularity of blood flow oscillations (BFOs) was quantified using a multiscale entropy approach. The results showed that BFO was significantly more irregular in ASCI and AB compared to SSCI during the maximal vasodilation period. Our results also demonstrate that the difference in the regularity of BFOs between original SBF signal and phase-randomized surrogate time series was larger in ASCI and AB compared to SSCI. Our findings indicate that SCI causes a loss of complexity of BFOs and exercise training may improve complexity in people with SCI. This study demonstrates that multiscale entropy is a sensitive method for detecting differences between different categories of people with SCI and might be able to detect effects of exercise training related to skin blood flow.

452 Booting Up the Brain: Intracranial Electroencephalography Reveals Cognitive Network Dynamics During Anesthesia Emergence

INTRODUCTION: The dynamic interplay between several large-scale brain networks is thought to underlie human consciousness. The default mode network (DMN) governs internal thought during rest, the central executive network (CEN) drives goal-directed problem solving, and the salience network (SN) detects and integrates new stimuli during the transition between resting and cognitively active states. While scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have elucidated much about these networks in steady states, intracranial EEG (iEEG) dynamics during state transitions remain largely unclear. METHODS: Seven patients with medically refractory epilepsy underwent robot-assisted stereotactic placement of depth electrodes for epileptic focus localization. Recordings from 422 total iEEG contacts representing 49 distinct cognitive network nodes were obtained during anesthesia emergence. Local-field dynamics and Observer’s Assessment of Alertness/Sedation (OAAS) scale state were analyzed in 20s clips for complexity, functional connectivity (FC), and graph communicability metrics. RESULTS: Signal complexity as represented by mean multiscale sample entropy (MSE) of SN and DMN nodes increases significantly during emergence from anesthesia (P < 0.05), driven by a shift from low- to mid-frequency power in key nodes. Intermediate consciousness states demonstrated higher mean MSE and participation coefficient, representing early integrative network behavior, before reaching an optimal balance of segregation and integration at wakefulness. During emergence, between-network FC increased most strongly between SN and CEN nodes. Communicability of SN nodes increased during emergence, driven predominately by increasing communicability of right anterior insula. CONCLUSIONS: Intracranial electroencephalographic recording of brain activity during anesthesia emergence demonstrates a complex but quantifiable interplay of default mode, central executive, and salience network hubs. There may be a greater than previously understood role of right anterior insula in gating transitions in consciousness states.

Fatigue condition diagnosis of rolling bearing based on normalized balanced multiscale sample entropy

Rolling bearing is a key component of machinery, its fatigue failure will affect the reliability of machinery. The bearing vibration signal has strong nonlinearity, resulting in weak fault information. Multiscale sample entropy (MSE) is an effective technique for analyzing nonlinear time series. However, MSE has limitations, such as a large deviation and weak information mining abilities, and even the calculation results have no definition. To this end, a normalized balanced multiscale sample entropy (NBMSE) is proposed in this study. For NBMSE, the data are first normalized to zero mean and unit standard deviation by zero-mean normalization, which eliminates the influence of abnormal data. Secondly, the balanced multiscale approach is advanced to coarse-grain the time series, which takes into account the uniqueness of different amplitudes and the globality of the time series. The method not only avoids no-definition results, but also reduces the entropy calculation error, thus mining useful amplitude information. The comparison of synthetic signals shows that the proposed NBMSE is more robust than other methods. Furthermore, the results of two bearing cases show that NBMSE not only provides sensitive features for fatigue diagnosis but also has higher diagnostic accuracy.

Fault diagnosis of real-scenario battery systems based on modified entropy algorithms in electric vehicles

Entropy algorithm is an efficient fault diagnosis technology gradually gaining popularity, which is highly promising in terms of battery safety protection in electric vehicles. This paper compares two entropy algorithms commonly used for battery fault diagnosis and their ability to diagnose abnormal fluctuations implied in the pre-fault phase. By exploring the influence of key calculation factors in the entropy algorithm on the fault diagnosis effect, this study pioneered the discovery of a significant normal distribution pattern and the logarithmic relationship between the diagnostic effect and the size of the Shannon entropy's calculation window and multi-scale sample entropy's scale factor, respectively. The relationship between computation time and computation factor was found to provide a theoretical basis for improving diagnosis efficiency through the rational selection of computation factors. Furthermore, based on actual vehicle fault data verification, a multi-level diagnosis strategy with strong robustness and generality is proposed using statistical methods. More importantly, this study continues to explore the importance of deeper mining of different entropy features, and the proposed control strategies are of high practical significance for battery fault diagnosis/prognosis in real-world vehicle applications.

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

Abstract Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.

Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?

So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.

Long-Term Effects and Potential Impact of Early Nutrition with Breast Milk or Infant Formula on Glucose Homeostasis Control in Healthy Children at 6 Years Old: A Follow-Up from the COGNIS Study.

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.

Online Chatter Identification for Thin-Walled Parts Machining Based on Improved Multisensor Signal Fusion and Multiscale Entropy

Thin-walled workpieces frequently appear to chatter during milling, causing the creation of vibration patterns on the surface of the workpiece, which affects the machining quality and workpiece performance. Identification of online chatter is important for thin-walled parts. The vibration signal associated with chatter changes dynamically throughout the milling process due to changes in tool position, removal of workpiece material, and transmission-related vibration signal attenuation characteristics, making reliable chatter identification difficult. This research proposes a unique online chatter identification approach for the machining of thin-walled parts based on improved multi-sensor signal fusion and multi-scale entropy. In order to acquire the fused vibration signals, the vibration signals produced during the machining of thin-walled parts are first acquired by sensors at various test sites and adaptively given weight coefficients by the improved Hausdorff distance and distance factor. Second, using the multi-scale sample entropy (MSSE) and multi-scale weighted permutation entropy (MSWPE), the eigenvalues of the fused vibration signals are obtained. Finally, the trained Logistic regression (LR) classification model is used to determine the vibration status of thin-walled parts. The analysis’s results demonstrated that the technique can properly identify the vibration state of thin-walled parts, and that the fusion signal can reflect the vibration state at the tool contact site more accurately.

Drivers’ Workload Electroencephalogram Characteristics in Cognitive Tasks Based on Improved Multiscale Sample Entropy

Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.

Intelligent diagnosis method of bearing fault based on ICEEMDAN and Ghost-IRCNN

As a critical component of rotating machinery, the health status of bearings is of great significance to ensure the safety of machine operation. Since the vibration signals are interfered by noise during the machine operation, the traditional bearing fault diagnosis model has insufficient feature extraction ability, low diagnostic accuracy, and slow convergence speed. An intelligent bearing fault diagnosis method is proposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and inverted residual convolution neural network integrated with Ghost module (Ghost-IRCNN). Firstly, ICEEMDAN is used to decompose the signals, and the kurtosis-correlation coefficient-margin factor index is proposed to filter and reconstruct the important modal components. Then, the multi-scale sample entropy is used to construct the feature vectors. Finally, the Ghost module, convolution block attention module, and inverted residual bottleneck are combined to build Ghost-IRCNN to realize bearing fault classification and diagnosis. The experimental results show that the proposed method can extract bearing fault features effectively, and the diagnosis accuracy can reach more than 98.80% under complex conditions.

Cavitation Noise Signal Classification of Hydroturbine Based on Improved Multi-Scale Symbol Dynamic Entropy

Cavitation is a phenomenon in the operation of hydroturbine, which is related to the operation efficiency and service life of the turbine. To identify both the cavitation noise signal and the non-cavitation noise signal, prevent damage as soon as possible, and avoid irreversible damage to the hydroturbine, a new paradigm based on multi-scale information entropy is proposed in this paper. The new proposed classification model combines improved multi-scale symbol dynamic entropy (IMSDE) and least square support vector machine (LSSVM). Improved multi-scale symbol dynamic entropy is utilized to learn features from the cavitation noise signal, and then the classifier of the least square support vector machine is used to classification. Multi-scale sample entropy (MSE), multi-scale permutation entropy (MPE) and multi-scale symbol dynamic entropy (MSDE) are selected as the contrast algorithms. According to the experimental results of four different operating conditions, IMSDE has the highest recognition rate. The average recognition rate of IMSDE is higher than that of MSDE, MSE and MPE. There is no significant difference in computational efficiency of IMSDE, MSDE and MPE. In conclusion, the IMSDE method proposed in this paper is superior to MSDE, MSE and MPE, for meeting the requirements of cavitation noise signal classification.

Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph

As a crucial method of the feature extraction, the complexity measurement has a wide range of applications in the field of nonlinear time series research. This paper presents an innovative multiscale sample entropy for measuring the complexity of time series based on the horizontal visibility graph. The modified multiscale sample entropy has been proven to be robust on two artificial time series, and is capable of reducing the undefined entropy generated as a result of the increase in scale. We apply the modified multiscale sample entropy to the diagnosis of epilepsy. Using a novel data processing algorithm that combines frequency bands with decomposition, feature vectors are constructed for Electroencephalography (EEG) signals through the proposed entropy calculation algorithm, and different classes of subjects are categorized based on K-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. In the meantime, we propose an improved horizontal visibility graph-based multiscale cross-sample entropy method to measure the synchronization between two time series. It shows robustness in artificial data and decreases the appearance of undefined entropy to a certain extent. It is possible to extract the characteristics of sleep EEG signals and divide the subjects’ sleep stages using this method. Furthermore, this paper introduces the surrogate data test and the proposed methods have the ability to detect the nonlinearity and synchronization in simulations and in real-world experiments. Experimental results demonstrate that the two proposed frameworks are effective in monitoring human health and in assessing physical status through EEG signals.

A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications

The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.

The Correlation Between College Students’ Interpersonal Skills and Their Entrepreneurial Performance

For college students, interpersonal skills (IS) are very necessary for them to build social networks and gain entrepreneurial resources, the level of their IS has a certain influence on their entrepreneurial performance (EP), and it’s needful to explore the mechanism of such influence. For this reason, this paper studied the correlation between college students’ IS and EP. At first, the paper gave a theoretical framework for analyzing the said influence mechanism. Then, focusing on the synchronization of IS and EP, this paper used the entropy to measure the sync correlation between the two, so as to quantify the joint improvement speed of the two, and six quantitative indicators had been adopted in the paper, including multi-scale sample entropy, multi-scale fuzzy entropy, cross-sample entropy, transfer entropy, de-trend correlation coefficient, and temporal correlation. At last, relevant experiments were conducted and the analysis results of the correlation between the two were given.