Search Results (69)

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

How to identify the key nodes in a complex network is a major challenge. In this paper, we propose a Second-Order Neighborhood Entropy Fuzzy Local Dimension Spring Model (SNEFLD-SM). SNEFLD-SM model combines a variety of centrality methods based on spring model, such as second-order neighborhood centrality, betweenness centrality, and fractal dimension, to evaluate the importance of nodes. Fractal technology can effectively boost the framework’s proficiency in understanding network self-similarity and hierarchical structure in multi-scale complex networks. It overcomes the limitation of the traditional centrality method which only focuses on local or global information. The method introduces information entropy and node influence range; information entropy can effectively capture the local and global features of the network. The node influence rangecan increase the node importance distinction and reduce the calculation cost. Meanwhile, an attenuation factor is introduced to suppress the “rich-club” phenomenon. Tests on six networks show that SNEFLD-SM has higher accuracy in critical node detection than traditional methods. Furthermore, the application of information entropy further strengthens the model’s capability to recognize key nodes. Full article

As a critical precision transmission element in numerical control (NC) machines, the linear rolling guide (LRG) suffers from surface wear degradation, which significantly impairs machining accuracy and operational reliability. Despite its importance, effective identification methods for LRG degradation remain limited. In this study, a hybrid approach combining multi-scale fuzzy entropy (MFE) with a gray wolf-optimized random forest (GWO-RF) algorithm was proposed to identify the surface wear state of the LRG. Preload degradation and vibration signals were collected at three surface wear stages throughout the LGR’s service life. The vibration signals were decomposed and reconstructed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by multi-scale fuzzy entropy analysis of the reconstructed signals. After dimensionality reduction via kernel principal component analysis (KPCA), the processed features were fed into the GWO-RF model for classification. Experimental results demonstrated a recognition accuracy of 97.9%. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

Accurate detection of surface defects such as wear, cracks, and flaws in metallic components is critical for equipment reliability and longevity, representing a core challenge in surface integrity engineering. To solve the information loss, low estimation accuracy and poor noise immunity associated with Multiscale Dispersion Entropy (MDE) are utilized to address the sensitivity to parameter selection and overfitting susceptibility of the Least Squares Twin Support Vector Machines (LSTSVM). A brand new fault diagnosis method which combined Time Shift Multiscale Ensemble Fuzzy Dispersion Entropy (TSMEFuDE) with binary tree LSTSVM (BT LSTSVM) was proposed. Firstly, a time shift method based on Higuchi Fractal Dimension was introduced to TSMEFuDE, resolving the continuity loss between coarse-grained levels. Second, four mapping techniques, linear, NCDF, tansig and logsig, are introduced. This synergetic combination of each advantage results in the improvement of entropy output stability. Furthermore, triangular and trapezoidal membership functions are incorporated into dispersion patterns and abolished in the round function, therefore enhancing the boundaries between the classes after signal mapping to discrete classes. Lastly, the proposed BT LSTSVM algorithm decomposes the multi-classification problem to a binary classification problem, which promotes the robustness of the algorithm. Simulation experiments maintain that TSMEFuDE has stronger adaptability, higher stability, and better noise resistance. In the fault diagnosis experiment, when compared to the Multiscale Fuzzy Dispersion Entropy (MFuDE) combined with the BT TSVM method, the TSMEFuDE combined with BT LSTSVM method improved the accuracy of bearing fault diagnosis by 5.65% and 2.82%. Full article

Quantitative electroencephalography (QEEG) has emerged as a promising tool for detecting Alzheimer’s disease (AD). Among QEEG measures, Multiscale Fuzzy Entropy (MFE) shows great potential in identifying AD-related changes in EEG complexity. However, MFE is intrinsically linked to signal amplitude, which can vary substantially among EEG systems, and this hinders the adoption of this metric for AD detection. To overcome this issue, this study investigates different preprocessing strategies to make the calculation of MFE less dependent on the specific amplitude characteristics of the EEG signals at hand. This contributes to generalizing and making more robust the adoption of MFE for AD detection. To demonstrate the robustness of the proposed preprocessing methods, binary classification tasks with Support Vector Machines (SVMs), Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers are used. Performance metrics, such as classification accuracy and Matthews Correlation Coefficient (MCC), are employed to assess the results. The methodology is validated on two public EEG datasets. Results show that amplitude transformation, particularly normalization, significantly enhances AD detection, achieving mean classification accuracy values exceeding 80% with an uncertainty of 10% across all classifiers. These results highlight the importance of preprocessing in improving the accuracy and the reliability of EEG-based AD diagnostic tools, offering potential advancements in patient management and treatment planning. Full article

Efficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for feature extraction and a gray wolf optimization (GWO)-optimized least squares support vector machine (LSSVM) for classification. Firstly, the HRCMFE is developed for feature extraction, which combines the segmentation advantage of hierarchical entropy (HE) and the computational stability advantage of refined composite multiscale fuzzy entropy (RCMFE). Secondly, the hyperparameters of LSSVM are optimized by GWO using a proposed fitness function. Finally, fault diagnosis of the planetary gearbox is achieved by the optimized LSSVM using the HRCMFE-extracted features. Simulation and experimental study results indicate that the proposed method demonstrates superior effectiveness in both feature discriminability and diagnosis accuracy. Full article

In the human–exoskeleton interaction process, accurately recognizing gait phases is crucial for effectively assessing the assistance provided by the exoskeleton. However, due to the similarity in muscle activation patterns between adjacent gait phases, the recognition accuracy is often low, which can easily lead to confusion in surface electromyography (sEMG) feature extraction. This paper proposes a real-time recognition method based on multi-scale fuzzy approximate root mean entropy (MFAREn) and an Efficient Multi-Scale Attention Convolutional Neural Network (EMACNN), building upon the concept of fuzzy approximate entropy. MFAREn is used to extract the dynamic complexity and energy intensity features of sEMG signals, serving as the input matrix for EMACNN to achieve fast and accurate gait phase recognition. This study collected sEMG signals from 10 subjects performing continuous lower limb gait movements in five common motion scenarios for experimental validation. The results show that the proposed method achieves an average recognition accuracy of 95.72%, outperforming the other comparison methods. The method proposed in this paper is significantly different compared to other methods (p < 0.001). Notably, the recognition accuracy for walking in level walking, stairs ascending, and ramp ascending exceeds 95.5%. This method demonstrates a high recognition accuracy, enabling sEMG-based gait phase recognition and meeting the requirements for effective human–exoskeleton interaction. Full article

The use of wearable devices for sleep apnea detection is growing, but their limited signal resolution poses challenges for accurate diagnosis. This study explores the feasibility of using SpO₂ signals from wearable sensors for detecting sleep apnea and classifying its severity. We propose a novel multi-scale feature engineering approach, which extracts features from coarsely grained SpO₂ signals across timescales ranging from 1 s to 600 s. Our results show that traditional SpO₂ markers, such as the oxygen desaturation index (ODI) and Lempel–Zip complexity, lose their relevance with the Apnea–Hypopnea Index (AHI) at longer timescales. In contrast, non-linear features like complex entropy, sample entropy, and fuzzy entropy maintain strong correlations with AHI, even at the coarsest timescales (up to 600 s), making them well suited for low-resolution data. Multi-scale feature extraction improves model performance across various machine learning algorithms by alleviating model bias, particularly with the Bayes and CatBoost models. These findings highlight the potential of multi-scale feature engineering for wearable device applications where only low-resolution data are commonly available. This could improve accessibility to low-cost, at-home sleep apnea screening, reducing reliance on expensive and labor-intensive polysomnography. Moreover, it would allow even healthy individuals to proactively monitor their sleep health at home, facilitating the early identification of potential sleep problems. Full article

Intermittent faults occur randomly, last for short durations, and ultimately lead to permanent failures, threatening the safety and stability of analog circuits. Additionally, these faults are often hard to differentiate from noise-induced anomalies, resulting in incorrect disassembly and complicating circuit maintenance. To address these challenges, we propose a novel fault diagnosis method. The method uses an adjustable sliding window to extract multiscale fuzzy entropy features, mitigating the impact of normal data on entropy calculations for intermittent faults. The coarse granulation strategy of sliding point by point is applied to avoid information loss in short time series. The raw signal is then segmented and transformed into four statistical features, which are fused into comprehensive amplitude features via a self-attention mechanism. This comprehensive feature better captures amplitude variations than individual statistical features. Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. Ablation studies confirmed its effectiveness. Although the proposed method does not have the lowest diagnostic cost and the fastest detection time, the differences with state-of-the-art methods are minimal, and the proposed method achieves higher classification accuracy. Taken together, these findings demonstrate the superiority of the proposed method. Full article

Rotating mechanical systems (RMSs) are widely applied in various industrial fields. Intelligent fault diagnosis technology plays a significant role in improving the reliability and safety of industrial equipment. A new algorithm based on improved multiscale fuzzy entropy and support vector machine (IMFE-SVM) is proposed for the automatic diagnosis of various fault types in elevator rotating mechanical systems. First, the empirical mode decomposition (EMD) method is utilized to construct a decomposition model of the vibration data for the extraction of relevant parameters related to the fault feature. Secondly, the improved multiscale fuzzy entropy (IMFE) model is employed, where the scale factor of the multiscale fuzzy entropy (MFE) is extended to multiple subsequences to resolve the problem of insufficient coarse granularity in the traditional MFE. Subsequently, linear discriminant analysis (LDA) is applied to reduce the dimensionality of the extracted features in order to overcome the problem of feature redundancy. Finally, a support vector machine (SVM) model is utilized to construct the optimal hyperplane for the diagnosis of fault types. Experimental results indicate that the proposed method outperforms other state-of-the-art methods in the fault diagnosis of elevator systems. Full article

This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer’s disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension (m) and similarity criterion (r), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer’s disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy’s ability to detect Alzheimer’s disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer’s disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer’s disease detection. Full article

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention. Full article

Alzheimer’s disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research. Full article

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields. Full article