Multiscale Entropy Values Research Articles

Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder

BackgroundStudies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD). MethodsThe eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed. ResultsFor patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41–70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13–40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores. ConclusionsPatients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

Predictability and Complexity of Fine and Gross Motor Skills in Fibromyalgia Patients: A Pilot Study.

Fine and gross motor tasks are usually used to evaluate behavioral dysfunctions and can be applied to diseases of the central nervous system, such as fibromyalgia (FM). Non-linear measures have allowed for deeper motor control analysis, focusing on the process and on the quality of movement. Therefore, to assess uncertainty, irregularity, and structural richness of a time series, different algorithms of entropy can be computed. The aim of this study was to (i) verify the single-scale and multiscale entropy values in fine and gross motor movements and (ii) to verify whether fine and gross motor tasks are sensitive to characterizing FM patients. The sample consisted of 20 females (46.2 ± 12.8 years) divided in two groups, an experimental group with 10 FM subjects and a control group with 10 subjects without FM. Inertial sensors were used to collect the finger tapping test (FTT), walking, and sit-and-stand task data. Regarding fine motor skills, patients with FM showed a loss of structural richness (complexity), but they had information processing with greater control in the FTT, probably to simplify task execution and for correction of the movement. On the other hand, people without FM seemed to have more automatic control of the movement when performed with the preferred hand and exhibited similar difficulties to the FM group when performed with the non-preferred hand. Gross motor tasks showed similar entropy values for both groups. The results show that FM patients have movement controls primarily at the level of the motor cortex, whereas people without FM perform movement at the medullary level, especially in fine motor tasks, indicating that the FTT is sensitive to the presence of FM, especially when performed with the preferred hand.

Leakage identification for mineral air supply pipeline system based on joint noise reduction and ELM

This work presents a leakage identification framework for the coal mine air supply pipeline system based on joint noise reduction and extreme learning machine (ELM). First, leakage vibration signals from a pipeline system are sampled. Then, vibration signals are decomposed by parameter-optimized variational mode decomposition (POVMD), where the mode number is determined via the energy difference principle, and the separation point of low- and high-frequency modes are identified by mutual information entropy. Next, the wavelet denoising method based on an improved threshold function is implemented on high-frequency modes. Then denoised vibration signals are reconstructed by denoised high-frequency modes and low-frequency modes. Subsequently, multi-scale entropy values are extracted and fed into ELM for training and testing. In the laboratory environment, the ELM model with 132 hidden nodes and the Sigmoid function shows the highest testing accuracy (94.17%), indicating that the proposed method is more practical and effective than other methods.

Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life

Objective. We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration. Approach. We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks’ gestation) from term age to two years. We analyzed the complexity index and maximum value of MSE over increasing age, across brain regions, and in function of extrauterine life duration, and used correlation matrices as a metric of functional connectivity of the cerebral cortex. Main results. We found an increase of strong inter-channel correlation of MSE (R > 0.8) with increasing age. Regional analysis showed significantly increased MSE between 3 and 24 months of corrected age in the posterior and middle regions with respect to the anterior region. We found a weak relationship (adjusted R2 = 0.135) between MSE and extrauterine life duration. Significance. These findings suggest that brain functional connectivity increases with maturation during the first two years of life. EEG complexity shows regional differences with earlier maturation of the visual cortex and brain regions involved in joint attention than of regions involved in cognitive analysis, abstract thought, and social behavior regulation. Finally, our MSE analysis suggested only a weak influence of early extrauterine life experiences (prior to term age) on EEG complexity.

A novel approach for quantitative electrogram analysis for driver identification: Implications for ablation in persistent atrial fibrillation.

This study sought to study the feasibility, efficacy, and safety of using multiscale entropy (MSE) analysis to guide catheter ablation for persistent atrial fibrillation (PsAF) and predict ablation outcomes. We prospectively enrolled 108 patients undergoing initial ablation for PsAF. MSE was calculated based on bipolar intracardiac electrograms (iEGMs) to measure the dynamical complexity of biological signals. The iEGMs data were exported after pulmonary vein isolation (PVI), then calculated in a customed platform, and finally re-annotated into the CARTO system. After PVI, regions of the highest mean MSE (mMSE) values were ablated in descending order until AF termination, or three areas had been ablated. Baseline characteristics were evenly distributed between the AF termination (n = 38, 35.19%) and the non-termination group. The RA-to-LA mean MSE (mMSE) gradient demonstrated a positive gradient in the non-termination group and a negative gradient in the termination group (0.105 ± 0.180 vs. -0.235 ± 0.256, P < 0.001). During a 12-month follow-up, 29 patients (26.9%) had arrhythmia recurrence after single ablation, and 18 of them had AF (62.1%). The termination group had lower rates of arrhythmia recurrence (15.79 vs. 32.86%, Log-Rank P = 0.053) and AF recurrence (10.53 vs. 20%, Log-Rank P = 0.173) after single ablation and a lower rate of arrhythmia recurrence (7.89 vs. 27.14%, Log-Rank P = 0.018) after repeated ablation. Correspondingly, subjects with negative RA-to-LA mMSE gradient had lower incidences of arrhythmia (16.67 vs. 35%, Log-Rank P = 0.028) and AF (16.67 vs. 35%, Log-Rank P = 0.032) recurrence after single ablation and arrhythmia recurrence after repeated ablation (12.5 vs. 26.67%, Log-Rank P = 0.062). Marginal peri-procedural safety outcomes were observed. MSE analysis-guided driver ablation in addition to PVI for PsAF could be feasible, efficient, and safe. An RA < LA mMSE gradient before ablation could predict freedom from arrhythmia. The RA-LA MSE gradient could be useful for guiding ablation strategy selection.

Assessment of arteriosclerosis based on multiscale cross approximate entropy of human finger pulse wave.

Arteriosclerosis is one of the diseases that endanger human health. There is a large amount of information in pulse wave signals to reflect the degree of arteriosclerosis. The degree of arteriosclerosis is assessed by analyzing pulse wave signal and calculating multi-scale entropy values. A method based on the multiscale cross-approximate entropy of the pulse wave of the human finger is proposed to assess the degree of arteriosclerosis. A total of 86 subjects were divided into three groups. The data of 1000 pulse cycles were selected in the experiment, and the multiscale cross-approximate entropy was calculated for the climb time and pulse wave peak interval. Independent sample t-test analysis gives the small-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval as p< 0.001 in Groups 1 and 2. The large-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval is p< 0.017 in Groups 2 and 3. Using the proposed algorithm, the results showed that the small-scale cross-approximate entropy of climb time and pulse wave peak interval could reflect the degree of arteriosclerosis in the human body from the perspective of autonomic nerve function. The large-scale cross-approximate entropy of climb time and pulse wave peak interval confirmed the effect of diabetes on the degree of arteriosclerosis. The results demonstrate the multiscale cross-approximate entropy is a comprehensive index to evaluate the degree of human arteriosclerosis.

Magnetic-Acoustic Feature Extraction and Damage Fusion Evaluation of 45 Steel Specimens during Fatigue Process for Remanufacturing

To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components.

Brain function complexity during dual-tasking is associated with cognitive impairment and age.

Early diagnosis of cognitive impairment is important because symptoms can be delayed through therapies. Synaptic disconnections are the key characteristics of dementia, and through nonlinear complexity analysis of brain function, it is possible to identify long-range synaptic disconnections in the brain. We investigated the capability of a novel upper-extremity function (UEF) dual-task paradigm in the functional MRI (fMRI) setting, where the participant flexes and extends their arm while counting, to differentiate between cognitively normal (CN) and those with mild cognitive impairment (MCI). We used multiscale entropy (MSE) complexity analysis of the blood oxygen-level dependent time-series across neural networks and brain regions. Outside of the fMRI, we used the UEF dual-task test, while the elbow kinematics were measured using motion sensors, to record the motor function score. Results showed 34% lower MSE values in MCI compared to CN (p<.04 for all regions and networks except cerebellum when counting down by one; effect size = 1.35±0.15) and a negative correlation between MSE values and age (average r2 of 0.30 for counting down by one and 0.36 for counting backward by three). Results also showed an improvement in the logistic regression model sensitivity by 14-24% in predicting the presence of MCI when brain function measure was added to the motor function score (kinematics data). Current findings suggest that combining measures of neural network and motor function, in addition to neuropsychological testing, may provide an accurate tool for assessing early-stage cognitive impairment and age-related decline in cognition.

Texture analysis using two-dimensional permutation entropy and amplitude-aware permutation entropy

Entropy algorithms have been applied extensively for time series analysis. The entropy value given by the algorithm quantifies the irregularity of the data structure. For higher irregular data structures, the entropy is higher. Both permutation entropy (PE) and amplitude-aware permutation entropy (AAPE) have been previously used to analyze time series. These two metrics have the advantage, over others, of being computationally fast and simple. However, fewer entropy measures have been proposed to process images. Two-dimensional entropy algorithms can be used to study texture and analyze the irregular structure of images. Herein, we propose the extension of AAPE for two-dimensional analysis (AAPE2D). To the best of our knowledge, AAPE2D has never been proposed to analyze texture of images. For comparison purposes, we also study the two-dimensional permutation entropy (PE2D) to analyze the effect of the amplitude consideration in texture analysis. In this study, we compare AAPE2D method with PE2D in terms of irregularity discrimination, parameters sensitivity, and artificial texture differentiation. Both AAPE2D and PE2D appear to be interesting entropy-based approaches for image texture analysis. When applied to a biomedical dataset of chest X-rays with healthy subjects and pneumonia patients, both methods showed to statistically differentiate both groups for P<0.01. Finally, using a SVM model and multiscale entropy values as features, AAPE2D achieves an average of 75.7% accuracy which is slightly better than the results of PE2D. Overall, both entropy algorithms are promising and achieve similar conclusions. This work is a new step towards the development of other entropy-based texture measures.

Gait performance in toddlers born preterm: A sensor based quantitative characterization

Background and ObjectivesPreterm children have an increased risk of motor difficulties. Gait analysis and wearable technologies allow the assessment of motor performance in toddlers, identifying early deviations from typical development. Using a sensor-based approach, gait performance of full-term and preterm toddlers at different risk of motor delay was analysed. The aim was to measure quantitative differences among groups. MethodsTwenty-nine two-year old children born preterm (≤36 gestational weeks) and 17 full-term controls, matched for age and walking experience, participated in the study. Preterm children were further divided based on risk of motor delay: preterm at high risk (n = 8, born at ≤28 gestational weeks or with ≤1000 g of body weight), and at moderate risk (n = 21).Children were asked to walk along a corridor while wearing 3 inertial sensors on the lower back and on the ankles. Gait temporal parameters, their variability, and nonlinear metrics of trunk kinematics (i.e. recurrence quantification analysis, multiscale entropy) were extracted from the collected data and compared among groups. ResultsChildren born preterm showed significantly longer stance and double support phases, higher variability of temporal parameters, and lower multiscale entropy values than peers born full-term. No difference was found for the other parameters when comparing preterm and full-term children. When comparing children grouped according to risk of delay, with increasing risk, children showed longer stride-, stance- and double-support-time, higher variability of temporal parameters, higher recurrence- and lower multiscale entropy values. ConclusionsSensor-based gait analysis allowed differentiating the gait performance of preterm from full-term toddlers, and of preterm toddlers at different risk of motor delay. When analysing the present results with respect to the expected trajectory of locomotor development, children born preterm, in particular those at higher risk of motor delay, exhibited a less mature motor control performance during gait: lower stability (i.e. longer support phases), and higher variability, although not structured towards the exploration of more complex movements (i.e. higher recurrence- and lower multiscale entropy values). These indexes can serve as biomarkers for monitoring locomotor development and early detecting risk to develop persistent motor impairments.

An efficient hybrid model for appliances classification based on time series features

Today, depending on the increasing population and technological developments, household appliances have significantly increased. This situation causes an ever-increasing energy demand, especially in modern societies. However, efficient energy use is also critical due to limited energy resources. With the Non-intrusive load monitoring methods, the usage of home appliances can be controlled, and behaviours can be adjusted for the energy saving of users. The control of home appliances is only possible with the effective detection of these appliances. In this study, a new hybrid model including a sliding window approach, long short-term memory (LSTM) network, and classifier is proposed to detect house appliances. Unlike traditional feature extraction, the proposed model uses time-series features to determine device features. For this purpose, each time series is divided into windows that do not overlap with the sliding window approach. The mean, standard deviation, median, and multiscale entropy values are calculated from each window. These values are combined and applied to the LSTM network for deep feature extraction. It is applied to k nearest neighbor (k-NN), Ensemble Learning (EBT), and support vector machine (SVM) classifiers to detect residential appliances from deep features retrieved from the LSTM network. The proposed model's effectiveness has been tested with the high-resolution profiles of the residential appliances dataset. In experimental studies, residential appliances were detected in LSTM based k-NN, EBT, and SVM classifiers with an accuracy of 98.25%, 97.81%, and 97.38%, respectively.

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were ‘projected’ on the regions of the Default Mode Network (DMN), the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.

Complexity analysis of the brain activity in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) due to cognitive loads/demands induced by Aristotle's type of syllogism/reasoning. A Power Spectral Density and multiscale entropy (MSE) analysis

ObjectiveWe aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders), ADHD (attention-deficit/hyperactivity disorder), compared with healthy subjects during the performance of an innovative cognitive task: Aristotle's valid and invalid syllogisms. We follow the Neuroanatomical differences type of criterion in assessing the results of our study in supporting or not the dual-process theory of Kahneman, 2011) (Systems I & II of thinking). MethodWe recorded EEGs from 14 scalp electrodes in 30 adults with ADHD, 30 with ASD and 24 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), the Aristotle's four types of syllogism mentioned above. The multiscale entropy (MSE), a nonlinear information-theoretic measure or tool was computed to extract features that quantify the complexity of the EEG. ResultsThe dynamics of the curves of the grand average of MSE values of the ADHD and ASD participants was significantly in higher levels for the majority of time scales, than the healthy subjects over a number of brain regions (electrodes locations), during the performance of both valid and invalid types of syllogism. This result is seemingly not in accordance of the broadly accepted ‘theory’ of complexity loss in ‘pathological’ subjects, but actually this is not the case as explained in the text. ADHD subjects are engaged in System II of thinking, for both Valid and Invalid syllogism, ASD and Control in System I for valid and invalid syllogism, respectively. A surprising and ‘provocative’ result of this paper, as shown in the next sections, is that the Complexity-variability of ASD and ADHD subjects, when they face Aristotle's types of syllogisms, is higher than that of the control subjects. An explanation is suggested as described in the text. Also, in the case of invalid type of Aristotelian syllogisms, the linguistic and visuo-spatial systems are both engaged ONLY in the temporal and occipital regions of the brain, respectively, of ADHD subjects. In the case of valid type, both above systems are engaged in the temporal and occipital regions of the brain, respectively, of both ASD and ADHD subjects, while in the control subjects only the visuo-spatial type is engaged (Goel et al., 2000; Knauff, 2007). ConclusionBased on the results of the analysis described in this work, the differences in the EEG complexity between the three groups of participants lead to the conclusion that cortical information processing is changed in ASD and ADHD adults, therefore their level of cortical activation may be insufficient to meet the peculiar cognitive demand of Aristotle's reasoning. SignificanceThe present paper suggest that MSE, is a powerful and efficient nonlinear measure in detecting neural dysfunctions in adults with ASD and ADHD characteristics, when they are called on to perform in a very demanding as well as innovative set of cognitive tasks, that can be considered as a new diagnostic ‘benchmark’ in helping detecting more effectively such type of disorders. A linear measure alone, as the typical PSD, is not capable in making such a distinction. The work contributes in shedding light on the neural mechanisms of syllogism/reasoning of Aristotelian type, as well as toward understanding how humans reason logically and why ‘pathological’ subjects deviate from the norms of formal logic.

Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG.

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay (), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy.

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

Discrimination of Severity of Alzheimer’s Disease with Multiscale Entropy Analysis of EEG Dynamics

Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer’s disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer’s disease.

Alterations of Cardiovascular Complexity during Acute Exposure to High Altitude: A Multiscale Entropy Approach

Stays at high altitude induce alterations in cardiovascular control and are a model of specific pathological cardiovascular derangements at sea level. However, high-altitude alterations of the complex cardiovascular dynamics remain an almost unexplored issue. Therefore, our aim is to describe the altered cardiovascular complexity at high altitude with a multiscale entropy (MSE) approach. We recorded the beat-by-beat series of systolic and diastolic blood pressure and heart rate in 20 participants for 15 min twice, at sea level and after arrival at 4554 m a.s.l. We estimated Sample Entropy and MSE at scales of up to 64 beats, deriving average MSE values over the scales corresponding to the high-frequency (MSEHF) and low-frequency (MSELF) bands of heart-rate variability. We found a significant loss of complexity at heart-rate and blood-pressure scales complementary to each other, with the decrease with high altitude being concentrated at Sample Entropy and at MSEHF for heart rate and at MSELF for blood pressure. These changes can be ascribed to the acutely increased chemoreflex sensitivity in hypoxia that causes sympathetic activation and hyperventilation. Considering high altitude as a model of pathological states like heart failure, our results suggest new ways for monitoring treatments and rehabilitation protocols.

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.

Uses of empirical mode decomposition and multi-entropy techniques to establish the correlations among vibrations, friction coefficients and component wear of ball-bearing-like specimens

Ball-bearing-like specimens with two groove factors (GFs) are prepared to investigate surface roughness (Ra) effects on tribological parameters and vibrations. The self-adaptive decomposition (SAD) and the intrinsic mode function (IMF) are used to evaluate the multi-scale entropy (MSE). Define entropy tolerance (ET) as the difference in maximum and minimum MSE values. ET variations for the grove-like vibrations created at various IMFs are obtained. Average friction coefficients (μ¯) and wear losses (WL) are evaluated. (μ¯)outer ≪ (μ¯)inner ≪ (μ¯)spacer are the controlling factors for average vibration (AV). Increasing the ball-spacer gap (GD) and lowering GF can reduce AV. Smaller AV, AET, and (WL)spacer results are obtained from relatively smaller (μ¯)spacer. The developments of IMF and MSF provide a novel and efficient way to identify bearing’s contact components governing the vibrations and tribological parameters of the tribo tests, and further establish the correlations for the result of AV, AET, μ¯, and (WL)spacer.

Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG.

Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims toexplore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. Thisstudy is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p=0.022), right parietal (0.31 vs. 0.13, respectively, p=0.017), left parietal (0.37 vs. 0.17, respectively, p=0.018), and central cortical area (0.36 vs. 0.21, respectively, p=0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.