Sample Entropy Research Articles

Sparse representation-based noise reduction for BOTDR monitoring signals in foundation pit anchor cables

Abstract In foundation pit monitoring, the Brillouin optical time-domain reflectometer (BOTDR) system typically has a low signal-to-noise ratio (SNR). To solve this problem, a BOTDR signal noise-suppression method based on sparse representation is proposed in this paper. The initial dictionary is constructed from the eigenvectors of the normalized graph Laplace matrix, and K-SVD and OMP algorithms are combined to update the dictionary and coefficient matrix to sparsely represent the essential characteristics of the BOTDR signal, filtering out random noise lacking sufficient similarity data and improving the quality of the reconstructed signal. To verify the effectiveness of the proposed algorithm, a BOTDR temperature-sensing experimental system was designed and used to test the denoising performance of the sparse representation algorithm quantitatively. The experimental results showed that the average SNR of the algorithm on six temperature levels was 27.4%, 15.4%, 13.1%, and 17.9% higher than that of WDD, EMT, EMD, and the raw signals, respectively. The average sample entropy (SE) was 24.4%, 16.0%, 15.4%, and 47.9% lower than that of WDD, EMT, EMD, and the raw signals, respectively. Additionally, the proposed Laplace-based dictionary outperformed the DCT dictionary. Finally, the monitoring project of the Beijing Stomatological Hospital was used as a test site. The sparse representation algorithm was used to conduct noise reduction experiments on the on-site anchor cable fiber optic monitoring signals. The average signal SE decrease of monitoring signals at the site reached 45.2%. This study provides an effective signal denoising method for the application of BOTDR technology in foundation pit monitoring.

Associations between less knee kinematic variability and worse patient-reported outcomes following anterior cruciate ligament reconstruction

ABSTRACT Individuals with anterior cruciate ligament reconstruction (ACLR) exhibit less knee kinematic variability while walking than uninjured controls, associated with deleterious changes in cartilage composition linked to an increased risk for early knee osteoarthritis (KOA). It is unknown whether less knee kinematic variability is also associated with worse knee-related patient-reported outcomes (PROs) consistent with KOA development. This study examined associations between kinematic variability during gait and PROs in individuals post-ACLR. Gait kinematics and the Knee Injury and Osteoarthritis Outcome Score (KOOS) were collected from 45 participants 6-months post-ACLR (67% Females; 21.45 ± 4.56 years). Overground gait biomechanics using 3D motion capture were collected, and knee kinematics were extracted for post-processing. Sample entropy (SampEn) was used to calculate knee kinematic variability. Pearson’s product-moment correlations were conducted to determine the associations between SampEn and KOOS sub-scores. Additionally, independent samples t-tests were performed to evaluate potential differences in SampEn outcomes between individuals with and without clinically relevant symptoms (defined in the introduction). Less sagittal plane kinematic variability is associated with greater pain (r = 0.37, p = 0.01) and symptoms (r = 0.32, p = 0.03). Symptomatic participants demonstrated less sagittal plane knee kinematic variability compared to asymptomatic participants (p = 0.01). The findings suggest less variable gait patterns 6-months post-ACLR may be linked to KOA-related symptoms.

Adaptive MSSRCPF filtering based on constrained optimization and fading memory for SINS/GNSS integrated navigation

To address particle degeneracy and the challenge of selecting importance density functions in traditional particle filters for complex nonlinear systems, we propose an adaptive mixed-order spherical simplex-radial cubature particle filter algorithm based on constrained optimization and fading memory. This algorithm integrates constrained optimization, an adaptive fading memory strategy, adaptive adjustment of particle numbers and weights, and the advantages of mixed-order spherical simplex-radial cubature Kalman filtering. By designing the importance density function using a mixed-order integration method, the algorithm significantly improves filtering accuracy over traditional cubature Kalman filters while maintaining lower computational complexity than high-order cubature Kalman filter methods. The adaptive fading memory strategy dynamically adjusts the covariance matrix, enhancing sensitivity to current measurement information and reducing the influence of historical data. By dynamically adjusting the noise covariance matrix and constraining the ratio between the error covariance and measurement noise covariance, the algorithm improves the convergence speed and accuracy of state estimation. An adaptive particle number and weight adjustment strategy based on effective sample size and entropy regularization dynamically adjusts the number of particles to reduce computational complexity while ensuring filtering accuracy, and employs entropy regularization to suppress weight over-concentration, thereby reducing the impact of outliers on the filtering results. Simulation results demonstrate that in complex SINS/GNSS integrated navigation systems, especially under high-noise and nonlinear conditions, the proposed algorithm significantly improves positioning accuracy compared to the CPF method, with the maximum latitude error reduced by approximately 54.8%, the maximum longitude error reduced by approximately 40.5%, and the maximum altitude error reduced by approximately 68.3%. Compared to the FMCPF method, the proposed algorithm also shows clear advantages in positioning accuracy, convergence speed, and computational efficiency, validating its effectiveness and practicality in complex environments.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

A hybrid model based on complementary ensemble empirical mode decomposition, sample entropy and long short-term memory neural network for the prediction of time series signals in NPPs

Accurate and reliable predictions are fundamental for the condition monitoring and maintenance of components and systems in nuclear power plants (NPPs). In this work, we propose a hybrid condition prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SampEn) and optimized long short-term memory (LSTM) neural network. Firstly, the CEEMD decomposes the time series signals into multiple subsequences called intrinsic mode functions (IMFs), by doing so, the complexity of the time series signals can be reduced, and this facilitates the accurate prediction of the original signals. Then, in order to reduce the calculation cost of prediction models for subsequences, SampEn is used to measure the complexities of the IMFs, and the IMFs whose values of SampEn are lower than the average are aggregated into a new component. Finally, the LSTM with the hyperparameters optimized by the Bayesian optimization algorithm (BOA) is used to perform the prediction of each component. The prediction results of the original signals are reconstructed by synthesizing the predictions of all components. The proposed hybrid prediction model is utilized on the time series signals collected from an NPP. The results obtained show that the proposed approach can capture the characteristics of the signals and has better performance in prediction accuracy than other models.

Intraoperative short-term blood pressure variability and postoperative acute kidney injury: a single-center retrospective cohort study using sample entropy analysis

BackgroundTo investigate if intraoperative very short-term variability in blood pressure measured by sample entropy improves discrimination of postoperative acute kidney injury after noncardiac surgery.MethodsAdult surgical patients undergoing general, thoracic, urological, or gynecological surgery between August 2016 to June 2017 at Seoul National University Hospital were included. The primary outcome was acute kidney injury stage 1, defined by the Kidney Disease: Improving Global Outcomes guidelines. Exploratory and explanatory variables included sample entropy of the mean arterial pressure and standard demographic, surgical, anesthesia and hypotension over time indices known to be associated with acute kidney injury respectively. Random forest classification and L1 logistic regression were used to assess four models for discriminating acute kidney injury: (1) Standard risk factors which included demographic, anesthetic, and surgical variables (2) Standard risk factors and cumulative hypotension over time (3) Standard risk factors and sample entropy (4) Standard risk factors, cumulative hypotension over time and sample entropy.ResultsTwo hundred and thirteen (7.4%) cases developed postoperative acute kidney injury. The median and interquartile range for sample entropy of mean arterial pressure was 0.34 and [0.26, 0.42] respectively. C-statistics were identical between the random forest and L1 logistic regression models. Results demonstrated no improvement in discrimination of postoperative acute kidney injury with the addition of the sample entropy of mean arterial pressure: Standard risk factors: 0.81 [0.76, 0.85], Standard risk factors and hypotension over time indices: 0.80 [0.75, 0.85], Standard risk factors and sample entropy of mean arterial pressure: 0.81 [0.76, 0.85] and Standard risk factors, sample entropy of mean arterial pressure and hypotension over time indices: 0.81 [0.76, 0.86].ConclusionAssessment of very short-term blood pressure variability does not improve the discrimination of postoperative acute kidney injury in patients undergoing non-cardiac surgery in this sample.

The Influence of Dopaminergic Medication on Regularity and Determinism of Gait and Balance in Parkinson’s Disease: A Pilot Analysis

Background/Objectives: Understanding how dual-tasking and Parkinson’s disease medication affect gait and balance regularity can provide valuable insights to patients, caregivers, and clinicians regarding frailty and fall risk. However, dual-task gait and balance studies in PD most often only employ linear measures to describe movement regularity. Some have used nonlinear techniques to analyze PD performances, but only in the on-medication state. Thus, it is unclear how the nonlinear aspects of gait or standing balance are affected by PD medication. This study aimed to assess how dopaminergic medication influenced the regularity and determinism of joint angle and anterior–posterior (AP) and medial–lateral (ML) center of pressure (COP) path time-series data while single- and dual-tasking in PD. Methods: Sixteen subjects with PD completed single- and dual-task gait and standing balance trials for 3 min off and on dopaminergic medication. Sample entropy and percent determinism were calculated for bilateral hip, knee, and shoulder joints, and the AP and ML COP path. Results: There were no relevant medication X task interactions for either the joint angles series or the balance series. Instead, the results supported independent effects of medication, dual-tasking, or standing with eyes closed. Balance task difficulty (i.e., eyes open vs. eyes closed) was detected by the nonlinear analyses, but the nonlinear measures yielded opposing results such that standing with eyes closed simultaneously yielded less regular and more deterministic signals. Conclusions: When juxtaposed with previous findings, these results suggest that medication-induced functional improvements in people with PD might be accompanied by a shift from lesser to greater signal consistency, and the effects of dual-tasking and standing with eyes closed were mixed. Future studies would benefit from including both linear and nonlinear measures to better describe gait and balance performance and signal complexity in people with PD.

Complexity and synchronization of carbon and new energy markets based on multiscale entropy

AbstractThis study provides a comprehensive analysis of the dynamic evolution of complexity and synchrony between the carbon market and the new energy market from 2019 to 2023, employing multiscale sample entropy and cross‐sample entropy methods. The study shows that, both in the long term and short term, the complexity of the new energy market consistently exceeds that of the carbon market. Additionally, the carbon and new energy markets exhibit higher synchronization on smaller scales. As the time scale increases, interactions between the carbon and new energy markets become more complex and diverse. According to these findings, it is recommended that policymakers improve the transparency of the new energy market, optimize the operational mechanisms of the carbon market, and ensure that carbon reduction strategies and new energy policies are mutually coordinated. This holds significant importance for both carbon reduction targets and the development of new energy.

Automatic calculation of the parasympathetic, sympathetic and Baevsky stress indexes provides a more comprehensive assessment of cardiac autonomic modulation

Abstract Background Cardiac autonomic modulation (CAM) is clinically evaluated by measuring the Heart Rate Variability (HRV) parameters, related to sympathetic and parasympathetic efferences to the heart, with linear (L), nonlinear (NL), and time-varying (TV) mathematical methods. Recently, automatic calculation of the parasympathetic (PNSi), sympathetic (SNSi), and Baevsky’s stress (BSTRi) indexes [1,2], integrating some L and NL parameters, has become available for more synthetic assessment of CAM. This study aimed to evaluate in healthy subjects (HS) the normal range of PNSi, SNSi, and BSTRi and their physiological variations during daily activity (ACT) and Non-REM sleep (NREM), taking also into account aging-related changes. Methods 24-hour (H24) Holter ECG recordings of 87 HS, divided into three (Age1, Age2 and Age3) groups (16-34, 35-52, and 53-84 years old, respectively), were retrospectively analyzed with the Kubios software (version 4.0), which automatically calculates the PNSi, SNSi, and BSTRi [1], and time domain (TD), frequency domain (FD), NL and TV HRV parameters. HRV was calculated from H24 and short-term (2 minutes) intervals, selected during ACT and NREM. All HRV calculations were done after "detrending" with the "smooth priors" function (lambda=500). Statistical analysis was performed with SPSS 21. Results Average values of the three indexes and several independent L and NL parameters are summarized in Table 1. Average BSTRi values of each age group were not different between ACT and NREM, whereas significant (p<0.05) increments between Age3 and Age1 (during ACT) and between Age3 and both Age1 and 2 (during NREM and H24) were found. Average SNSi values were slightly above the normality range [1] during ACT, or normal (during NREM and H24). Average PNSi values were within the suggested normality range [1] with minimal aging-related variations. Significant aging-related decrement of the selected TD and FD parameters was found in all analysed conditions. Among NL parameters, the correlation dimension (D2) values showed significant aging-related decrement (H24 and NREM), while H24 and ACT sample entropy (SampEn) was lower in Age1 and Age2, compared with Age3 (p<0.05). An example (H24 analysis of Age3 data) of significant (p<0.01) correlation among the three indexes and the selected independent linear and non-linear parameters is shown in Figure 1. Conclusions In this study, a significant aging-related increment of BSTRi was found in all investigated conditions. PNSi and SNSi were always within the suggested normality range [1]. In spite of the significant changes of linear parameters consistent with the well-known aging-related decrement of HRV, the higher SampEn (and BSTRi) found in Age3 HS suggests that an "healthy elderly", is characterized by a more complex adaptation of CAM, likely mediated also by the observed moderate (healthy?) increment of physiological stress. However further investigation on a broader population is warranted.Table 1Figure 1

Anchoring temporal convolutional networks for epileptic seizure prediction.

Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the Anchoring Temporal Convolutional Networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.08 per hour and an average prediction time (APT) of 99.98 minutes. Using the CHB-MIT scalp EEG dataset, we a achieve 97.44% sensitivity, an FPR of 0.11 per hour, and an APT of 92.99 minutes. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The average prediction time of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improving patients' quality of life.

Age-Related Changes in Postural Stability in Response to Varying Surface Instability in Young and Middle-Aged Adults

As individuals transition into middle age, subtle declines in postural control may occur due to gradual reductions in neuromuscular control. The current study aimed to examine the effect of age on bipedal postural control across three support surfaces with varying degrees of instability: a firm surface, a foam pad, and a multiaxial balance board. The effect of surface stability was also assessed. Postural accelerations were recorded using a tri-axial accelerometer placed over the lumbar spine (L5) in 24 young female adults (23.9 ± 5.3 years) and 24 middle-aged female adults (51.4 ± 5.9 years). Sample entropy (SampEn) was used to analyze the complexity of postural control by measuring the regularity of postural acceleration. The main results show significant age-related differences in the mediolateral and anteroposterior acceleration directions (p ≤ 0.012). Young adults exhibit more irregular fluctuations in postural acceleration (high SampEn), reflecting greater efficiency or automaticity in postural control compared to middle-aged adults. Increased surface instability also progressively decreases SampEn in the mediolateral direction (p < 0.001), reflecting less automaticity with increased instability. However, no interaction effects are observed. These findings imply that incorporating balance training on unstable surfaces might help middle-aged adults maintain postural control and prevent future falls.

Research on underwater target signal orientation estimation based on smoothness priors approach

Purpose This paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones. Design/methodology/approach Initially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer. Findings Simulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation. Originality/value To the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift.

An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction

Accurate PV power prediction is crucial for stable grid operation and rational dispatch. However, due to the instability of PV power generation, PV power prediction still has great challenges. Therefore, an Autoformer model based on secondary decomposition, Bayesian optimization and error correction for PV power prediction. In order to reduce the complexity of the data and fully extract the features, two decomposition methods are employed. First, empirical mode decomposition (EMD) is applied to decompose the PV power series at the first level. Then, the sample entropy (SE) is introduced to measure the complexity of each component, and the variational mode decomposition (VMD) is employed to implement secondary decomposition of the component with the highest complexity. Secondly, a Bayesian optimization algorithm enhanced Autoformer model is developed for predicting each component, and the predicted component results are aggregated to obtain preliminary PV power prediction results. Finally, the preliminary prediction results are error corrected using a least squares support vector machine. A four-month PV dataset from a PV power plant in Hangzhou, China is utilized to validate the effectiveness of the proposed model. The experimental results show that the model after primary decomposition is superior to the single model, and the prediction accuracy is substantially improved after secondary decomposition. The proposed model has the best prediction performance in predicting the PV power for different seasons, which shows good robustness.

Dynamical complexity and multifractal analysis of geomagnetic activities at high temporal scales over three solar cycles

Activities in geospace occur at different time scales. Understanding geomagnetic activity at high temporal scales will give insight into fast dynamics in geospace. This study aims to investigate dynamical complexities in geomagnetic activities at a high temporal scale across three solar cycles. Geomagnetic activities, as represented by 5-min SYM-H data, were considered in this study over three solar cycles (22–24) from 1986 to 2019. Chaos analysis using sample entropy, Lyapunov exponent, and correlation dimension indicates that the geomagnetic activities are driven by intrinsic complex and chaotic processes. Positive Lyapunov exponent values between 0.13 and 0.18, 0.15–0.18, and 0.16–0.19 were obtained for solar cycles 22, 23, and 24 respectively. Furthermore, geomagnetic activities were also found to have multifractal structures driven by high fractal exponents with fine structures. A positive relationship was obtained between the annual mean values of SYM-H and the degree of complexity. It is concluded that geomagnetic activities have a short prediction horizon.

Near-infrared light stimulation regulates neural oscillation and memory behavior of mice with Alzheimer's disease.

Photobiomodulation (PBM) is a non-invasive neuromodulation technique for the brain. Low-intensity near-infrared light (1-500 mw) has demonstrated the ability to improve memory in Alzheimer's disease (AD) model mice, suggesting its potential for AD treatment. However, the impact of PBM on neural oscillations in the hippocampal region affected by AD remains unknown. In this study, AD model mice were subjected to PBM for 60 days and then tested using novel object recognition behavior (NOR) experiments. During behavioral experiments, local field potential signals (LFP) of the mice was recorded using a single electrode in the CA1 region to analyze memory ability and neural oscillation characteristics. The results revealed that mice stimulated with PBM exhibited significantly higher new object differentiation indices compared to the Sham group (p < 0.01). Furthermore, PBM stimulation led to a significant increase in relative power and sample entropy of theta and gamma bands (p < 0.01). The coupling intensities of θ-low-γ and θ-high-γ were also significantly higher in the PBM group compared to the Sham group (p < 0.01). In conclusion, these findings suggest that PBM may improve memory ability in AD mice through regulation of neural oscillation characteristics, providing a theoretical basis for utilizing PBM as a treatment modality for Alzheimer's disease.

Cognitive computing method based on decoding psychological emotional states

The current artificial intelligence is constrained to passively executing human command control. It cannot perceive, learn, and guide itself. Furthermore, the majority of these systems are unable to comprehend the human psychological cognitive state or emotional intention actively. To enhance the universality and generalization ability of existing emotion recognition methods, a psychological emotion state decoding method combining singular spectrum analysis and entropy measure is designed based on an electroencephalography signal. This method is developed to empower the cognitive computing system with the ability to perceive emotions. The entropy measure is accelerated by the vector dissimilarity criterion. Finally, dynamic sample entropy model learning is used to realize the cognitive calculation of psychological and emotional state recognition. The results demonstrated that the computational efficiency of the proposed decoding algorithm was significantly improved. The average recognition accuracy of positive and negative emotional states reached 82.78±16.22. The best recognition accuracy of the proposed electroencephalography emotion recognition method was 84.64 %. The proposed electroencephalography emotion recognition method demonstrated an accuracy of 64.15 % in recognizing cross-individual positive, neutral, and negative emotional states. The proposed emotion recognition method shows better universality and generalization performance, and can effectively identify people's psychological and emotional states.

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

Comparative Analysis of Linear and Nonlinear sEMG Methods for Detecting Muscle Fatigue During Dynamic Biceps Curls

Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established linear and nonlinear methods in measuring muscle fatigue caused by dynamic contractions through surface electromyography (sEMG) signals. A group of twelve healthy individuals completed biceps curls at a consistent pace of one repetition per four seconds, which constituted 75% of their 10-repetition maximum. Concurrently, sEMG signals were captured from the biceps brachii muscle at 1000 Hz. To assess the sEMG signals during the initial, middle, and final sets of 10 repetitions, three linear metrics—mean frequency, median frequency, and spectral moment ratio (SMR)—along with two nonlinear approaches, namely sample entropy and detrended fluctuation analysis (DFA), were utilized. The study's outcomes indicated notable shifts in the SMR values and the two DFA-derived scaling exponents across the exercise sets. These results indicated that SMR, sample entropy, and DFA are effective in gauging muscle fatigue, with sample entropy and DFA demonstrating heightened sensitivity to the fatigue levels when compared to the linear metrics.

CONTINUOUS SCORING OF AUTISM SPECTRUM DISORDER PATIENTS BY ANALYZING THEIR EEG SIGNALS

Clinical manifestations and standard psychological tests have been widely used to diagnose autism spectrum disorder (ASD) patients and evaluate their severity level. The gold-standard criterion to diagnose ASD patients is the childhood autism rating scale (CARS), which is a qualitative questionnaire that is filled out through a systematic interview while no physiological test/record is performed to determine this score. To make the diagnosis process quantitative, electroencephalography (EEG) signals have been repeatedly analyzed to differentiate healthy subjects from ASD patients. However, the precise relationship between the abnormal behavior of EEG signals and different ASD severity levels is not well investigated. Here, we use CARS to qualitatively determine the severity level of 14 autistic children, who voluntarily enrolled in our study. We recorded their EEG signals from 19 scalp channels when they were awake in the idle state and elicited three informative features including approximation entropy, multiscale entropy and sample entropy in successive time frames. Among the three measures of entropy, the last one exhibits the highest sensitivity, where its correlation coefficient (CC) exceeds 0.7, on the electrode positions T6 (CC [Formula: see text] 0.74), P4 (CC [Formula: see text] 0.76) and Cz (CC [Formula: see text] 0.75). Results of sample entropy in channels Cz-versus-Pz and P4-versus-FP2 show that a simple K-nearest neighbor classifier can provide 93% classification accuracy among patients with mild, moderate and severe ASD levels. Comparing the proposed method to the conventional ones, we also extracted power spectral density features from the channels but they failed to identify the ASD severity level with an acceptable accuracy.

Multi-image encryption scheme using cross-plane coupling permutation and plain-by-plain wave diffusion

Images contain a wealth of visual information, are susceptible to unauthorized access due to their vulnerability and sensitivity. This paper designs a novel multi-image encryption scheme for protecting the privacy of images of different sizes and types. Initially, a 2D memristive hyperchaotic map (2D-MHM) is designed and subjected to various dynamic analyses and randomness evaluations. The results demonstrate that the proposed map possesses an exceptionally large parameter space, high Lyapunov exponent and sample entropy, and has successfully passed the entire suite of NIST test, verifying its feasibility for confidential communication. Then we present a multi-image encryption scheme combining cross-plane coupling permutation and plain-by-plain wave diffusion to realize random exchange and global variation of pixels in different planes. The performance evaluation and numerical analysis demonstrate that the scheme is resilient against multifarious types of attacks, possesses great security while effectively enhancing encryption efficiency. Finally, the proposed scheme is compared with advanced algorithms and its application in healthcare is discussed, exhibiting its superiority in multiple aspects.