Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer's disease

Azami, Hamed; Rostaghi, Mostafa; Fernández, Alberto; Escudero, Javier

doi:10.1109/embc.2016.7592197

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…The results demonstrated that DisEn, unlike PerEn, WPerEn, and MPerEn, is sensitive to changes in simultaneous frequency and amplitude values and bandwidth of signals [1]. We also showed that DisEn outperformed PerEn in the discrimination of diverse biomedical datasets [1], [10].…”

Section: Introductionmentioning

confidence: 72%

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals

2017

IEEE Trans. Biomed. Eng.

229

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Section: Introductionmentioning

confidence: 72%

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals

2017

IEEE Trans. Biomed. Eng.

229

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“…As DispEn needs to neither sort the amplitude values of each embedding vector nor calculate every distance between any two composite delay vectors with embedding dimensions m and

, it is fast [ 9 ]. The good performance of DispEn to distinguish different dynamics of real-time series was also shown in [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 82%

Amplitude- and Fluctuation-Based Dispersion Entropy

Azami

Escudero

2018

Entropy

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159

160

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Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel-Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.

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“…(3) Introduce the embedding dimension m and time delay d, and construct the time series z m,c i as follows [17,18]: [19]. As the signal with m member is divided into c classes, the number of dispersion patterns corresponds to c m .…”

Section: Multiscale Dispersion Entropy Based On Rms (Mderms)mentioning

confidence: 99%

Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy

et al. 2018

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As one of the most important components in rotating machinery, it's necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MDErms) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MDErms is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MDErms is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (MFEVS). The MFEVS keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the MFEVS, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault.

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Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer's disease

Cited by 18 publications

References 23 publications

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals

Amplitude- and Fluctuation-Based Dispersion Entropy

Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy

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