Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection

Djemili, Rafik; Djemili, Ilyes

doi:10.1080/10255842.2023.2271603

Cited by 9 publications

(5 citation statements)

References 78 publications

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“…The VMD algorithm searches for an optimal solution through iterative search, decomposes the original signal into K intrinsic modal components (IMF), and ensures that the sum of all modal components is equal to the size of the original data while minimizing the sum of all modal components' bandwidths [10].…”

Section: General Framework Of Transformer Top Oil Temperature Predict...mentioning

confidence: 99%

Transformer top oil temperature prediction based on VMD and optimized BiGRU model

Bai,

Guo,

Niu

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Transformer is one of the important equipment of power system, in order to grasp the operation state of transformer in advance and maintain the safe and stable operation of power system, the predict of top oil temperature of transformer is necessary. Aiming at the problems of low accuracy and poor generalization of current transformer top oil temperature prediction models, one CNN-BiGRU transformer oil temperature prediction model based on variational mode decomposition (VMD), dung beetle optimization (DBO),convolutional neural network (CNN) and bidirectional gated recurrent uni (BiGRU) algorithm is proposed in this paper. The experiment is carried out based on the real transformer data of a place in northwest China, the performance of the proposed model was evaluated quantitatively by root mean square difference (RMSE), mean absolute error (MAE), and R-squared (R 2 ).The experimental results show that the rootmean-square error of the proposed model is reduced by nearly 60% compared with the original transformer top oil temperature prediction model, which shows better performance and could provide theoretical reference for transformer top oil temperature prediction under different situations.

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Section: General Framework Of Transformer Top Oil Temperature Predict...mentioning

confidence: 99%

Transformer top oil temperature prediction based on VMD and optimized BiGRU model

Bai,

Guo,

Niu

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…In this section, consider applying the proposed method to practical problems to verify if MF-LSSVM-DFA is more effective than traditional MF-DFA. EEG, as typical nonlinear signals, have been widely utilized by scholars for analytical research [44][45][46]. This section conducts an empirical analysis of EEG data from the Epilepsy Laboratory at the University of Bonn in Germany.…”

Section: Mf-lssvm-dfa For Eeg Signal Classificationmentioning

confidence: 99%

Modified MF-DFA Model Based on LSSVM Fitting

Wang,

Zhong,

Yue

et al. 2024

Fractal Fract

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This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

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“…The analysis of EEG signals poses significant challenges due to their nonlinearity, non-stationarity, and susceptibility to interference. Currently, commonly used methods for EEG signal analysis include time domain, frequency domain, time–frequency domain analysis, and nonlinear dynamical analysis [ 7 ]. In recent years, EEG signals have been widely applied in stress recognition [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

Dong,

Xu,

Zheng

et al. 2024

Brain Sciences

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This paper proposes a new hybrid model for classifying stress states using EEG signals, combining multi-domain transfer entropy (TrEn) with a two-dimensional PCANet (2D-PCANet) approach. The aim is to create an automated system for identifying stress levels, which is crucial for early intervention and mental health management. A major challenge in this field lies in extracting meaningful emotional information from the complex patterns observed in EEG. Our model addresses this by initially applying independent component analysis (ICA) to purify the EEG signals, enhancing the clarity for further analysis. We then leverage the adaptability of the fractional Fourier transform (FrFT) to represent the EEG data in time, frequency, and time–frequency domains. This multi-domain representation allows for a more nuanced understanding of the brain’s activity in response to stress. The subsequent stage involves the deployment of a two-layer 2D-PCANet network designed to autonomously distill EEG features associated with stress. These features are then classified by a support vector machine (SVM) to determine the stress state. Moreover, stress induction and data acquisition experiments are designed. We employed two distinct tasks known to trigger stress responses. Other stress-inducing elements that enhance the stress response were included in the experimental design, such as time limits and performance feedback. The EEG data collected from 15 participants were retained. The proposed algorithm achieves an average accuracy of over 92% on this self-collected dataset, enabling stress state detection under different task-induced conditions.

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Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection

Cited by 9 publications

References 78 publications

Transformer top oil temperature prediction based on VMD and optimized BiGRU model

Transformer top oil temperature prediction based on VMD and optimized BiGRU model

Modified MF-DFA Model Based on LSSVM Fitting

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

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