Multiscale entropy based study of the pathological time series

This paper studies the multiscale entropy （MSE） of electrocardiogram＇s ST segment and compares the MSE results of ST segment with that of electrocardiogram in the first time. Electrocardiogram complexity changing characteristics has important clinical significance for early diagnosis. Study shows that the average MSE values and the varying scope fluctuation could be more effective to reveal the heart health status. Particularly the multiscale values varying scope fluctuation is a more sensitive parameter for early heart disease detection and has a clinical diagnostic significance.

Multiscale entropy:A tool for understanding the complexity of postural control

Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time scales can lead to alterations in postural center of pressure(Co P)fluctuations.Multiscale entropy(MSE) has been used to identify differences in fluctuations of postural Co P time series between groups with and without known physiological impairments at multiple time scales.The purpose of this paper is to:1) review basic elements and current developments in entropy techniques used to assess physiological complexity;and 2) identify how MSE can provide insights into the complexity of physiological systems operating at multiple time scales that underlie the control of posture.We review and synthesize evidence from the literature providing support for MSE as a valuable tool to evaluate the breakdown in the physiological processes that accompany changes due to aging and disease in postural control.This evidence emerges from observed lower MSE values in individuals with multiple sclerosis,idiopathic scoliosis,and in older individuals with sensory impairments.Finally,we suggest some future applications of MSE that will allow for further insight into how physiological deficits impact the complexity of postural fluctuations;this information may improve the development and evaluation of new therapeutic interventions.

Integration of multiscale entropy and BASED scale of electroencephalography after adrenocorticotropic hormone therapy predict relapse of infantile spasms

Background Even though adrenocorticotropic hormone(ACTH)demonstrated powerful efficacy in the initially successful treatment of infantile spasms(IS),nearly half of patients have experienced a relapse.We sought to investigate whether features of electroencephalogram(EEG)predict relapse in those IS patients without structural brain abnormalities.Methods We retrospectively reviewed data from children with IS who achieved initial response after ACTH treatment,along with EEG recorded within the last two days of treatment.The recurrence of epileptic spasms following treatment was tracked for 12 months.Subjects were categorized as either non-relapse or relapse groups.General clinical and EEG recordings were collected,burden of amplitudes and epileptiform discharges(BASED)score and multiscale entropy(MSE)were carefully explored for cross-group comparisons.Results Forty-one patients were enrolled in the study,of which 26(63.4%)experienced a relapse.The BASED score was significantly higher in the relapse group.MSE in the non-relapse group was significantly lower than the relapse group in theγband but higher in the lower frequency range(δ,θ,α).Sensitivity and specificity were 85.71%and 92.31%,respectively,when combining MSE in theδ/γfrequency of the occipital region,plus BASED score were used to distinguish relapse from non-relapse groups.Conclusions BASED score and MSE of EEG after ACTH treatment could be used to predict relapse for IS patients without brain structural abnormalities.Patients with BASED score≥3,MSE increased in higher frequency,and decreased in lower frequency had a high risk of relapse.

Revisiting the relationship between composite multiscale entropy and THz optical parameters with exterior product

With the framework of exterior product,we investigate the relationship between composite multiscale entropy[CMSE]and refractive index and absorption coefficient by reanalyzing six concentrations of bovine serum albumin aqueous solutions from the published work.Two bivectors are constructed by CMSE and its square by the refractive index and absorption coefficient under vectorization.The desirable linear behaviors can be captured,not only between the defined two bivectors in normalized magnitudes,but also between the normalized magnitude of bivectors pertinent to CMSE and the magnitude of a single vector on the refractive index or absorption coefficient,with the processing of optimum selection.Besides that,the relationship between the coefficients of two bivectors is also considered.The results reveal that plenty of sound linear behaviors can be found and also suggest the scale of 15,16 and frequency of 0.2,0.21 THz are prominent for those linear behaviors.This work provides a new insight into the correlation between terahertz[THz]time and frequency domain information.

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Complexity analysis of the Portevin-Le Chatelier in an Al alloy atdifferent tem peratures

The complexity of the Portevin-Le Chatelier(PLC)effect in an Al alloy at different temperatures was ana-lyzed by modified multiscale entropy.The results show that three evolutions of entropy with scale factor,i.e.,near zero,monotonically increasing and peak-shape,were observed corresponding to the smoothcurves,type-A serrations and type-B/-C serrations,respectively.The scale factor at the peak was one-third of the average serration period.The sample entropy increased initially and then decreased withtemperature,which was opposite to the critical strain.It is also suggested that the type-A serrations cor-responded to self-organized criticality and the type-B/-C serrations corresponded to chaos through theevolutions of entropy with scale factor.

A comparative study on ApEn,SampEn and their fuzzy counterparts in a multiscale framework for feature extraction

Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.

Applications of dynamical complexity theory in traditional Chinese medicine

Traditional Chinese medicine （TCM） has been gradually accepted by the world. Despite its widespread use in clinical settings, a major challenge in TCM is to study it scientifically. This difficulty arises from the fact that TCM views human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. As a result, conventional tools that are based on reductionist approach arc not adequate. Methods that can quantify the dynamics of complex integrative systems may bring new insights and utilities about the clinical practice and evaluation of efficacy of TCM. The dynamical complexity theory recently proposed and its computational algorithm, Multiscale Entropy （MSE） analysis, are consistent with TCM concepts. This new system level analysis has been successfully applied to many health and disease related topics in medicine. We believe that there could be many promising applications of this dynamical complexity concept in TCM. In this article, we propose some promising applications and research areas that TCM practitioners and researchers can pursue.