Refined Multiscale Hilbert–Huang Spectral Entropy and Its Application to Central and Peripheral Cardiovascular Data

Humeau-Heurtier, Anne; Wu, Chiu-Wen; Wu, Shuen-De; Mahé, Guillaume; Ábrahám, Pierre

doi:10.1109/tbme.2016.2533665

Cited by 21 publications

(19 citation statements)

References 36 publications

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“…We observed different scales at which the maximum separation between groups occurred with DM0‐DM1 and DM0‐CB at scale τ = 15 and DM1‐CB at τ = 10. Similar behavior was shown by Humeau et al in a recent study when BF signals were filtered for frequencies associated with heart rate (~0.6‐2 Hz) using a multiscale entropy analysis. Age, BMI, and CVD risk were all associated with a reduction in complexity, or signal variability, at certain scales, the only common one being τ = 15.…”

Section: Discussionsupporting

confidence: 83%

Multi‐domain analysis of microvascular flow motion dynamics in NAFLD

et al. 2019

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Objective To determine whether analysis of microvascular network perfusion using complexity‐based methods can discriminate between groups of individuals at an increased risk of developing CVD. Methods Data were obtained from laser Doppler recordings of skin blood flux at the forearm in 50 participants with non‐alcoholic fatty liver disease grouped for absence (n = 28) or presence (n = 14) of type 2 diabetes and use of calcium channel blocker medication (n = 8). Power spectral density was evaluated and Lempel‐Ziv complexity determined to quantify signal information content at single and multiple time‐scales to account for the different processes modulating network perfusion. Results Complexity was associated with dilatory capacity and respiration and negatively with baseline blood flux and cardiac band power. The relationship between the modulators of flowmotion and complexity of blood flux is shown to change with time‐scale improving discrimination between groups. Multiscale Lempel‐Ziv achieved best classification accuracy of 86.1%. Conclusions Time and frequency domain measures alone are insufficient to discriminate between groups. As cardiovascular disease risk increases, the degree of complexity of the blood flux signal reduces, indicative of a reduced temporal activity and heterogeneous distribution of blood flow within the microvascular network sampled. Complexity‐based methods, particularly multiscale variants, are shown to have good discriminatory capabilities.

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Section: Discussionsupporting

confidence: 83%

Multi‐domain analysis of microvascular flow motion dynamics in NAFLD

et al. 2019

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“…1) The complexity of pink noise (1/f noise) is higher than white noise, whereas the irregularity or uncertainty of the former signal is lower than the latter [7], [8], [18]. Thus, white and pink noise are two suitable data for assessing the multiscale entropy techniques [7], [8], [14], [16], [29]. For more information about white vs. pink noise, please refer to [8], [30].…”

Section: A Synthetic Signalsmentioning

confidence: 99%

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

et al. 2019

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Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this article. MFDE is robust to the presence of baseline wanders, or trends, in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. MFDE dealt with the problem of undefined MSE values and, compared with MDE, led to more stable entropy values over the scale factors for pink noise. Overall, MFDE was the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along the signal (e.g., eye movement data). This study shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases recordings.

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“…Entropy has also been used to quantify disorder in a spectral domain based on the assumption of speech being more organized than noise [32,33]. Entropy has also found use in a speaker recognition system as approximate entropy [34] and in the biomedical field as the refined multi-scale Hilbert-Huang spectral entropy [35]. Shannon's entropy for information source measurement is defined in Equation (5).…”

Section: Entropy As a Information-theoretic Measuresmentioning

confidence: 99%

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Ong

Tan

Vengadasalam

et al. 2017

Entropy

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Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

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Refined Multiscale Hilbert–Huang Spectral Entropy and Its Application to Central and Peripheral Cardiovascular Data

Abstract: By showing better performances than existing algorithms to compute MSSE, our work is a new and promising way to compute an entropy measure from the spectral domain. It also has the advantage of stressing physiologically linked phenomena.

Cited by 21 publications

References 36 publications

Multi‐domain analysis of microvascular flow motion dynamics in NAFLD

Multi‐domain analysis of microvascular flow motion dynamics in NAFLD

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

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