Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease

Azami, Hamed; Kinney‐Lang, Eli; Ebied, Ahmed; Fernández, Alberto; Escudero, Javier

doi:10.1109/embc.2017.8037533

Cited by 28 publications

(23 citation statements)

References 17 publications

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“…Gómez et al [ 112 ] reported MSE profiles that represented the SampEn values of each coarse-grained time series relative to the scale factor. Azami et al [ 113 ] found that the values of multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE), and MSE in AD patients were lower than those in NCs at short scale factors, while at long scale factors, the MDE and MSE values from AD subject signals had higher values [ 112 ]. In contrast, the MPE values at long scale factors were very similar for AD patients and NCs.…”

Section: Resultsmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

101

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Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

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Section: Resultsmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

101

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“…However, mvMSE and mvMFE have the following shortcomings: (1) mvMSE and mvMFE values may be unreliable and unstable for short signals (300 sample points); (2) they are not quick enough for real-time applications; and (3) computation of mvMSE and mvMFE of a signal with a large number of channels needs to have large memory space, as shown later. To address these drawbacks and due to the advantages of multiscale dispersion entropy (DispEn-MDE) over the state-over-the-art multiscale entropy techniques in terms of distinguishing different kinds of dynamics of univariate synthetic and real time series and computation time [ 27 , 28 , 29 ], we propose four algorithms to extend our recently developed MDE to its multivariate forms, termed multivariate MDE (mvMDE). To evaluate the mvMDE methods, we use both synthetic and real multivariate datasets.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

Azami

Fernández

Escudero

2019

Entropy

Self Cite

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Objective: Due to the non-linearity of numerous biomedical signals, non-linear analysis of multi-channel time series, notably multivariate multiscale entropy (mvMSE), has been extensively used in biomedical signal processing. However, mvMSE has three drawbacks: 1) mvMSE values are either undefined or unreliable for short signals; 2) mvMSE is not fast enough for real-time applications; and 3) the computation of mvMSE for signals with a large number of channels requires the storage of a huge number of elements. Methods: To deal with these problems and improve the stability of mvMSE, we introduce multivariate multiscale dispersion entropy (MDE -mvMDE), as an extension of our recently developed MDE, to quantify the complexity of multivariate time series. Results: We assess mvMDE, in comparison with mvMSE and multivariate multiscale fuzzy entropy (mvMFE), on correlated and uncorrelated multichannel noise signals, bivariate autoregressive processes, and three biomedical datasets. The results show that mvMDE takes into account dependencies in patterns across both the time and spatial domains. The mvMDE, mvMSE, and mvMFE methods are consistent in that they lead to similar conclusions about the underlying physiological conditions. However, the proposed mvMDE discriminates various physiological states of the biomedical recordings better than mvMSE and mvMFE. In addition, for both the short and long time series, the mvMDE-based results are noticeably more stable than the mvMSE-and mvMFE-based ones. Conclusion: For short multivariate time series, mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is noticeably faster than mvMFE and mvMSE and also needs to store a considerably smaller number of elements. Significance: Due to its ability to detect different kinds of dynamics of multivariate signals, mvMDE has great potential to analyse various physiological signals.

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“…. When dealing with the fault of the bearing vibration signal, it is often difficult to know the most suitable time scales, and signal nonstationarity and irregularity tend to be very strong; in order to solve this problem well, Azami et al proposed multiscale discrete entropy (MDE) in 2017 [47] and proved that the MDE approach based on processing of the nonstationary signal has a strong ability of feature extraction. e flow of MDE is shown in Figure 3.…”

Section: Multiscale Dispersion Entropy (Mde)mentioning

confidence: 99%

“…Moreover, bearing fault diagnosis based on multiscale entropy has been widely used in the field of intelligent fault, such as multiscale fuzzy entropy (MFE) [44] and multiscale permutation entropy (MPE) [45]. Rostaghi and Azami have proposed dispersion entropy (DE) [46] and multiscale dispersion entropy (MDE) [47]. MDE does not need to sort the amplitude of each embedded vector nor does it need to calculate the distance between any two compound delay vectors with embedded dimensions m and m + 1, which makes DE and MDE faster than PerEn, PerEn, and MperEn, significantly.…”

Section: Introductionmentioning

confidence: 99%

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

et al. 2019

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In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.

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Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease

Cited by 28 publications

References 17 publications

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

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