Improved GP algorithm for the analysis of sleep stages based on grey model

Li, Yabing; Xie, Songyun; Zhao, Jin; Liu, Chang; Xie, Xinzhou

doi:10.2306/scienceasia1513-1874.2017.43.312

Cited by 6 publications

(4 citation statements)

References 23 publications

(25 reference statements)

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“…These features are typically calculated by applying a fast Fourier transform (FFT) to short-time window segments of EEG signals followed by further processing. Considering that the property of EEG signals is somewhat chaotic, in addition to the traditional features of the EEG signal, the chaotic features based on non-linear dynamical analysis are also highly recommended to investigate the dynamic characteristics of EEG (Li et al, 2017 ; Nawaz et al, 2020 ). In the current study, 12 time domain features, seven spectral features, and six chaotic features are extracted for further analysis, as shown in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction

Dong²

2023

Front. Neurosci.

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BackgroundK-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps.New methodIn this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection.ResultsExperimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F10-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2.Comparison to state-of-the-art methodsThe RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F10-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure.ConclusionIn summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.

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Section: Methodsmentioning

confidence: 99%

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction

Dong²

2023

Front. Neurosci.

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“…e GP algorithm is one e ective method of calculating the correlation dimension of chaotic time series [33]. Correlation dimension analysis is an important branch of fractal theory, and this dimension is used as an indicator to evaluate complex trends in data.…”

Section: Correlation Dimension Calculation With the Gp Algorithmmentioning

confidence: 99%

Coal Reservoir Characterization in a Tectonic Setting and the Effects of Tectonism on the Coalbed Methane (CBM) Content

Hou

Zhang

et al. 2019

Advances in Materials Science and Engineering

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Research on the relationships among tectonics, micropores, microfractures, and coalbed methane (CBM) content is important for the optimal selection of CBM production areas. In this study, micropore-microfracture structural parameters of coal samples from the Guojiahe coalfield are determined through the use of X-ray photography, an image recognition algorithm, and a liquid nitrogen adsorption method. The relationship between the micropore-microfracture characteristics of the reservoir and the gas content is quantitatively assessed using the Grassberger and Procaccia (GP) algorithm to calculate the correlation dimension of the parameters. Micropore-microfracture development varies in different tectonic zones. Additionally, the CBM content varies according to the characteristic parameters of hysteresis loops and the pore diameter. The correlation dimension is an effective indicator of the nonlinear relationship between reservoir micropore-microfracture characteristics and the gas content.

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“…Correlation dimension is a branch of fractal dimension, it has been widely used in signal processing because of simple calculation. In 1983, Grassberger and Procacca proposed the GP algorithm for calculating the correlation dimension of time series [ 43 ]. For the time series

, let the embedding dimension of reconstructed phase space is

, the delayed sampling is applied to a series with a delay

.…”

Section: The Proposed Noise Reduction Algorithmmentioning

confidence: 99%

Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Guan

Yang

2018

Entropy

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Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

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Improved GP algorithm for the analysis of sleep stages based on grey model

Cited by 6 publications

References 23 publications

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction

Coal Reservoir Characterization in a Tectonic Setting and the Effects of Tectonism on the Coalbed Methane (CBM) Content

Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

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