Multiscale Dispersion Entropy Research Articles

Research on Fault Diagnosis Method of Reciprocating Compressor Based on RSSD and Optimized Parameter RCMDE

As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method.

A novel epilepsy detection approach using intrinsic multiscale entropy analysis and DSEAM-enhanced 1D-ResNets

Abstract Epilepsy, a prevalent neurological disorder, typically requires a complex diagnostic process involving medical history inquiry, physical examination, head computed tomography, and electroencephalogram (EEG) visual examination. Among the existing epilepsy automated detection algorithms, machine learning methods require manual feature selection. Most of deep learning algorithms for automatic detection have high complexity and computational complexity. To address this issue, this study proposes a high-precision, robust, and low computational epilepsy automatic detection algorithm based on EEG signal processing. The study utilizes ensemble empirical mode decomposition to preprocess the original EEG signal, breaking it down into intrinsic mode functions (IMFs) across various frequency bands. These IMFs contain information about epilepsy occurrence within the signal at different frequency bands. To enhance computational efficiency and reduce data dimension, the refined composite multiscale dispersion entropy of each IMF is further computed at different scales, referred to as intrinsic multiscale entropy (IME) analysis. IME analysis consolidates epilepsy occurrence information from EEG signals across different frequency bands and scales, linking entropy values to generate feature vectors. Drawing inspiration from successful deep residual networks and Squeeze-and-Excitation (SE) networks, the study introduces a double SE attention module enhanced one-dimensional residual network to classify one-dimensional feature vectors. The proposed method was tested on epilepsy dataset from University of Bonn, and the results demonstrated superior classification performance. In the experiment, the distinction between normal and epileptic EEG signals achieved 100% accuracy rate, while distinguishing between normal, epileptic interval, and epileptic EEG signals achieved accuracy rate of 99.41%.

Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis.

To enhance the performance of entropy algorithms in analyzing complex time series, generalized Gaussian distribution improved permutation entropy (GGDIPE) and its multiscale variant (MGGDIPE) are proposed in this paper. First, the generalized Gaussian distribution cumulative distribution function is employed for data normalization to enhance the algorithm's applicability across time series with diverse distributions. The algorithm further processes the normalized data using improved permutation entropy, which maintains both the absolute magnitude and temporal correlations of the signals, overcoming the equal value issue found in traditional permutation entropy (PE). Simulation results indicate that GGDIPE is less sensitive to parameter variations, exhibits strong noise resistance, accurately reveals the dynamic behavior of chaotic systems, and operates significantly faster than PE. Real-world data analysis shows that MGGDIPE provides markedly better separability for RR interval signals, EEG signals, bearing fault signals, and underwater acoustic signals compared to multiscale PE (MPE) and multiscale dispersion entropy (MDE). Notably, in underwater target recognition tasks, MGGDIPE achieves a classification accuracy of 97.5% across four types of acoustic signals, substantially surpassing the performance of MDE (70.5%) and MPE (62.5%). Thus, the proposed method demonstrates exceptional capability in processing complex time series.

Research on rolling bearing fault diagnosis method based on SBOA-Optimized FMD

Abstract To address the challenges in extracting rolling bearing fault features and the parameter sensitivity of Feature Mode Decomposition (FMD), this study introduces a novel approach that integrates the Secretary Bird Optimization Algorithm (SBOA) with FMD. The proposed method leverages SBOA to identify the most suitable parameter settings for FMD, employing the minimum arrangement entropy as the objective function. The optimized FMD is then employed to process the raw signals. The most informative components are selected based on the correlation coefficient-cliffiness index. Their time-domain characteristics and multiscale dispersion entropy (MDE) are computed to construct the fault feature vectors. Finally, SVM with SBOA optimization is used for classification. Empirical results show that SBOA-FMD achieves better fitness values than other methods with the same number of iterations. The method achieves an average recognition accuracy of 99.45%, demonstrating its effectiveness.

Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images

Abstract The rolling bearing is a critical component of mechanical equipment, and its failure can lead to serious consequences. In order to effectively extract fault features of rolling bearings and improve fault diagnosis performance, a fault diagnosis framework based on hierarchical multiscale dispersion entropy (HMDE) and improved histogram of oriented gradient (HOG) is proposed by combining entropy method with image recognition method. Firstly, the original vibration signal is subjected to moving average filtering to eliminate sudden noise and outliers. Then, HMDE is used for the extraction of fault features. HMDE can evaluate the complexity of the signal at different levels and scales, thereby extracting more comprehensive information. Based on HMDE, entropy color block (ECB) images are generated and the improved HOG of the images are extracted. Finally, K-nearest neighbor (KNN) is used to classify the improved HOG features, completing the recognition of different working states of rolling bearings. The validity and robustness of the proposed fault diagnosis framework are proved by the verification experiments on the public bearing datasets of Case Western Reserve University and Southeast University.

Analysis and identification of gas-liquid two-phase flow pattern based on multivariate multi-scale dispersion entropy and interconnected dispersion pattern complex network

Gas-liquid two-phase flow is polymorphic and unstable. The investigation of the flow behavior of two-phase flow is vital for the application of gas-liquid transport in ocean engineering areas. We propose an analytical framework that combines multivariate multi-scale dispersion entropy (mvMDE) and interconnected dispersion pattern complex networks (IDPCN) for flow patterns. The resistance sensor array is used to capture the two-phase flow fluctuation signals in the riser, and the dimensionally reduced time series are nonlinearly mapped into a multivariate symbolic sequence. The mvMDE of the multivariate symbolic sequence is computed to measure the complexity of the flow pattern signals at the level of amplitude variation. In addition, a new multis-cale complex network based on the symbolic sequence is proposed to study the pseudo spatial coupling behavior of flow patterns. The evolution of the flow pattern from bubble to churn flow is quantitatively characterized using the mean value of the complex network indices in the optimal scale range. Finally, we construct the joint plane of mvMDE and two network indices to complete flow pattern identification. The research results show that the framework can effectively fuse multi-channel flow pattern information at different time scales to reveal the evolution mechanism of gas-liquid two-phase flow.

An integrated gear tooth crack analysis of coupled electromechanical model: A complexity-based approach

This paper presents a novel approach by conducting a complexity-based study of an electromechanical (EM) gearbox system with tooth root cracks, utilizing an analytical model and motor current signals for the first time, in contrast to existing studies that primarily use experimental vibration data for complexity-based analyses. The coupled EM model is developed using the modified Lagrangian approach and by incorporating improved time-varying mesh stiffness and gear crack models. Refined composite multiscale dispersion entropy (RCMDE) is employed to evaluate system complexity at various crack depths. The analysis focuses on residual current signals and uncovers an inverse correlation between RCMDE values and the severity of cracks. To demonstrate the efficacy of the proposed approach, a comparison study is conducted between RCMDE and multiscale dispersion entropy (MDE) on both residual current and vibration signals. The results show that RCMDE provides superior stability and effectiveness compared to MDE. Additionally, it has been found that residual current signals offer more robust insights into system complexity than residual vibration signals. This study contributes valuable insights into the behavior of gearbox systems experiencing crack-induced faults, especially concerning short-time series data, highlighting the advantages of using motor current signals and RCMDE for complexity-based analyses.

Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia

Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly individuals and between different dementia subtypes. Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young individuals. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance. Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.

A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Traffic flow (TF) prediction is vital in traffic systems. Aiming at the effect of noise on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an improved CEEMDAN based on amplitude-aware permutation entropy (AAPE), named ICEEMDAN, is proposed. Aiming at the parameter selection problems on variational mode decomposition (VMD), VMD improved by pelican optimization algorithm (POA) is proposed, called PVMD. Aiming at the parameter selection problems of bidirectional long short-term memory (BiLSTM) and kernel extreme learning machine (KELM), BiLSTM improved by sea-horse optimizer (SHO), named SHOBiLSTM, and KELM improved by nutcracker optimizer algorithm (NOA), named NOAKELM are proposed, respectively. Since the chaotic characteristics of TF, multi-factor combined TF prediction model with ICEEMDAN, PVMD, SHOBiLSTM, NOAKELM, improved entropy weight method is proposed. First, use ICEEMDAN to decompose TF and get intrinsic mode functions (IMFs). Second, refined composite multiscale dispersion entropy (RCMDE) is used to divide IMFs into the fuzzy and detail components, fuzzy components are secondarily decomposed by PVMD, IMFs and factors are multi-factor predicted by SHOBiLSTM and NOAKELM. Finally, improved entropy weight method is proposed to weight SHOBiLSTM and NOAKELM results. TF of the UK highway proves the superiority. Experiments indicate that the proposed model is better than other nine models with RMSE, MAE, MAPE and R2 of 29.5868, 22.0431, 0.0701 and 0.9947 for TF I. The outcomes suggest this proposed model precedes nine models at 99% confidence level, providing data basis and theoretical for travel plans and traffic system.

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

Refined composite multivariate multiscale fuzzy dispersion entropy: Theoretical analysis and applications

Multivariate multiscale sample entropy (mvMSE) is a popular method to quantify the irregularity of multi-channel time series. However, mvMSE generates either undefined or unreliable results for short-length signals and has a high computational cost. To address these issues, multivariate multiscale dispersion entropy (mvMDE) was recently developed, but it depends on its parameters and overlooks some data information. To address these limitations, we herein propose an extension of fuzzy dispersion entropy (FDE) by incorporating fuzzy set theory, multidimensional embedding reconstruction theory, and dispersion patterns. This leads to the multivariate multiscale FDE (mvMFDE) and refined composite mvMFDE (RCmvMFDE) measures. (RC)mvMFDE is the first multivariate analysis based on Shannon entropy that uses the fuzzification concept. Some sets of time series have been synthesized and analyzed using several concepts in multivariate signal processing to evaluate the advantages of our introduced mvMFDE and RCmvMFDE. The results indicated that the proposed methods are less sensitive to their parameters, signal length, and noise, providing more stable results compared to the existing approaches mvMSE and mvMDE. On multi-channel noise signals and bivariate autoregressive processes, mvMSE, mvMDE, and mvMFDE produced similar finding although mvMFDE exhibited superior discriminative capabilities. When applied to one physiological dataset and two mechanical datasets, RCmvMFDE distinguished the diverse dynamics of multichannel signals exceptionally well.

Improved multiscale coded dispersion entropy: A novel quadratic-coded health indicator of rolling bearings

Abstract Dispersion entropy (DE) is widely used to quantify the complexity of nonlinear time series. In order to improve the ability to capture fault characteristics, a novel approach called coded dispersion entropy (CDE) has been introduced in recent years. CDE aims to expand the number of possible dispersion patterns and enhance the encoding of similar dispersion patterns. However, the coding method of CDE ignores the amplitude differences between dispersion elements and average elements, resulting in the inaccurate assignment of samples. Additionally, CDE is unable to extract effective information from other time scales. These limitations hinder the ability of CDE to effectively characterize faults. This paper proposes an improved multiscale coded dispersion entropy (IMCDE) to solve these limitations. The method introduces the interval scaling factor "R" and utilizes the sum and difference between the mean element and R as new coding boundaries. This approach addresses the sensitivity issue of the CDE coding mode towards smaller amplitude differences by the reasonable quadratic coding mode. Additionally, a composite coarse-graining process is introduced to rearrange the first coarse-grained point in sequence, resulting in multiple sets of sequences. The average probability of the same dispersion pattern at each scale is calculated to correct the entropy error. The experimental results from two sets of bearing faults demonstrate the effectiveness of this method in extracting critical features associated with faulty bearings. Furthermore, the method showed higher classification accuracy compared to multiscale dispersion entropy (MDE) and multiscale coded dispersion entropy (MCDE). Additionally, it exhibited smaller classification error than MDE and MCDE.

A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi-stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), empirical mode decomposition based on time-varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN-EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU–CNN–EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high-complexity and low-complexity components by RCMDE. Next, secondly decompose the reconstructed high-complexity components into high-complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high-complexity parts and the low-complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions

Aiming at the problem that the fault information of rolling bearings under harsh operation environment is easily submerged by strong noise interference, which causes the traditional method to be difficult to identify bearing faults effectively, this paper proposes a novel robust intelligent fault diagnosis method for rolling bearings based on sparsity-assisted parameter adjustable variational mode decomposition (VMD) and whale optimization algorithm-based optimized least-squares support vector machine (WOA-LSSVM). Firstly, a sparsity measurement named the improved Gini index is introduced as the fitness function of grid search algorithm to adaptively adjust and search for the optimal decomposed mode number K and penalty factor α of VMD. Additionally, VMD containing the optimal parameters is adopted to decompose the original bearing vibration signal into several intrinsic mode function (IMF), and the effective signal reconstruction is performed by screening the sensitive IMF components according to the effective weighted kurtosis Gini index criterion. Subsequently, the refine composite multi-scale dispersion entropy of the reconstructed signal is further calculated to establish a multi-dimensional feature vector set. Finally, the constructed feature vector set is fed into the WOA-LSSVM to achieve automatic fault identification of rolling bearings. The effectiveness of the proposed method is verified by two experimental examples. Experimental results show that the proposed method has higher fault recognition accuracy and better robustness against noise than other homologous methods in noisy conditions. This study provides a new perspective for the developing of robust diagnosis methods.

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm's effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection.

Amplitude-based multiscale reverse dispersion entropy: a novel approach to bearing fault diagnosis

The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the characteristics from a variety of complex nonlinear signals, including bearing signals, due to its excellent performance to quantify the uncertainties of complex nonlinear systems. However, limited by the classification number and coarse-graining process, the periodic impulses generated by the defect point cannot be effectively detected by MFDE, restraining the characterization abilities of entropy features and resulting in undesirable diagnosis results for bearing faults. To overcome the disadvantages of MFDE, an amplitude-based multiscale dispersion entropy (AMDE) is proposed in this paper. The AMDE utilizes the phase scale factor to calculate multiple groups of amplitude difference series that contain different amplitude information. As such, the amplitude compression caused by the large-scale factor in traditional coarse-graining process is avoided, and the calculated entropy features not only characterize the irregularity of the whole signal but also reflect the changes of the impulse components. Afterwards, the perception range and the sensibility of AMDE are expanded and enhanced for amplitude variation, and the coarse-graining process and Gaussian reference are used to obtain multi-dimensional reversed entropy features. Combining those steps, the amplitude-based multiscale reverse dispersion entropy (AMRDE) algorithm is proposed. Finally, the capability of the proposed algorithm to track the amplitude variation and fluctuation is successfully demonstrated by analyzing noisy signals and amplitude-modulated signal. Meanwhile, the features extracted from bearing signals demonstrated that it is effective to use AMRDE to represent the health conditions of rolling bearing. Therefore, the entropy metric calculated by AMRDE can be the useful indicator in the fields of mechanical equipment fault diagnosis, structural health monitoring, and so on.

Optimization of epilepsy detection method based on dynamic EEG channel screening

To decrease the interference in the process of epileptic feature extraction caused by insufficient detection capability in partial channels of focal epilepsy, this paper proposes a novel epilepsy detection method based on dynamic electroencephalogram (EEG) channel screening. This method not only extracts more effective epilepsy features but also finds common features among different epilepsy subjects, providing an effective approach and theoretical support for across-subject epilepsy detection in clinical scenarios.Firstly, we use the Refine Composite Multiscale Dispersion Entropy (RCMDE) to measure the complexity of EEG signals between normal and seizure states and realize the dynamic EEG channel screening among different subjects, which can enhance the capability of feature extraction and the robustness of epilepsy detection. Subsequently, we discover common epilepsy features in 3–15 Hz among different subjects by the screened EEG channels. By this finding, we construct the Residual Convolutional Long Short-Term Memory (ResCon-LSTM) neural network to accomplish across-subject epilepsy detection.The experiment results on the CHB-MIT dataset indicate that the highest accuracy of epilepsy detection in the single-subject experiment is 98.523 %, improved by 5.298 % compared with non-channel screening. In the across-subject experiment, the average accuracy is 96.596 %. Therefore, this method could be effectively applied to different subjects by dynamically screening optimal channels and keep a good detection performance.

Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals

Slope entropy (SloEn) is an effective nonlinear dynamic method to represent the complexity of time series, which has been extensively applied to various mechanical signal processing. However, it is only applicable to the analysis of single-channel time series at a single scale. Additionally, thresholds γandδ of SloEn can affect the division of symbols. To address these limitations, this paper firstly develops multivariate SloEn (mvSloEn) and extends it to multiscale mvSloEn (mvMSloEn), which not only accounts for the correlation of time series complexity within and across channels, but also mirrors the complexity of multi-channel time series over multiple scales. Furthermore, the sea-horse optimization-mvMSloEn (SHO-mvMSloEn) is proposed through utilizing the sea-horse optimizer (SHO) to fine-tune the thresholds for improved performance. Finally, the proposed SHO-mvMSloEn is applied to three real-world datasets and the highest classification accuracies are all over 98 %, superior to the existing multivariate multiscale dispersion entropy (mvMDE), multivariate multiscale fuzzy entropy (mvMFE), and multivariate multiscale sample entropy (mvMSE), which demonstrates that the proposed SHO-mvMSloEn always exhibits the best feature extraction capability.

EEG-based detection of driving fatigue using a novel electrode

In view of the serious harm caused by driving fatigue, this paper investigated driving fatigue detection based on electroencephalogram (EEG) features, designed a novel semi-dry electrode for acquisitioning drivers' EEG signals, and performed refined composite multiscale fluctuation dispersion entropy (RCMFDE) feature extraction of the θ and β bands rhythm signals in the acquisitioned EEG signals. The research results showed that the novel semi-dry electrode designed in this paper had the advantages of convenient replenishment of conductive liquid, early warning of insufficient conductive liquid, comfortable to wear compared with the traditional wet and dry electrodes under the premise of being able to collect EEG signals with qualified quality. And compared with traditional methods such as multiscale sample entropy (MSE), multiscale permutation entropy (MPE), multiscale symbolic dynamic entropy (MSDE), multiscale dispersion entropy (MDE), and refined composite multiscale dispersion entropy (RCMDE), the EEG features extracted by the RCMFDE method in this paper have a more obvious fatigue feature tendency, and thus can identify the driving fatigue state more effectively.