Multivariate multiscale entropy: A tool for complexity analysis of multichannel data

Ahmed, Mosabber Uddin; Mandic, Danilo P.

doi:10.1103/physreve.84.061918

Cited by 300 publications

(335 citation statements)

References 38 publications

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“…7b that though bivariate white noise has higher complexity than 1/f noise for the first scale, the complexity becomes lower than 1/f noise for higher scales. This example on synthetic data illustrates, that by design, 1/f noise is structurally more complex than uncorrelated random noise, a result consistent with standard MSE/MMSE [2,14,15] as shown in Fig. 7a.…”

Section: Memd-enhanced Multivariate Multiscale Entropysupporting

confidence: 63%

“…7a shows the standard multivariate MSE analysis [14,15] for bivariate random white noise (uncorrelated), conforming with the interpretation that the MSampEn values monotonically decrease with scale, whereas for 1/f noise (long-range correlated) the MSampEn remains constant over multiple scales. This has a physical justification, as by design 1/f noise is structurally more complex than uncorrelated white noise.…”

Section: Standard Multivariate Multiscale Entropymentioning

confidence: 99%

“…This measure is very stable for data lengths of over several hundred samples [15] and has become a work horse in practical entropy estimation from real-world data. Multivariate sample entropy enables entropy calculation for multichannel data, by taking into account both within-and cross-channel dependencies, and was introduced only recently in [14]. To calculate multivariate sample entropy (MSampEn), recall from multivariate embedding theory [19], that for a p-variate time series {x k,i } N i=1 , k = 1, 2, .…”

Section: Multivariate Empirical Mode Decompositionmentioning

confidence: 99%

“…Recent multichannel extensions of both EMD [12] and MSE algorithm [13][14][15] have been shown to outperform their standard, univariate, counterparts in the analysis of real-world multivariate signals. The availability of these extensions provides an opportunity to develop a robust framework for the complexity analysis of multivariate data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate sample entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.

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Section: Memd-enhanced Multivariate Multiscale Entropysupporting

confidence: 63%

Section: Standard Multivariate Multiscale Entropymentioning

confidence: 99%

Section: Multivariate Empirical Mode Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

View full text Add to dashboard Cite

show abstract

“…This danger or any dangers that undermine cloud environment, it will be a noteworthy matter for clients and suppliers [60]. These assaults are hurtful to computing.…”

Section: Parameters Of Services Performance Declinationmentioning

confidence: 99%

Program Aging and Service Crash

Ahamad¹

2016

IJCATR

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Abstract:Program aging is a degradation of performance or functionality caused by resource depletion. The aging affects the cloud services which provide access to big data bank and computing resources. This suffers large budget and delays of defect removal, which requires other related solutions including renewal in the form of controlled restarts. Collection of various runtime metrics are more significant source for further study of detection and analysis of aging issues. This study highlights the method for detecting aging immediately after their introduction by runtime comparisons of different development scenarios. The study focuses on aging of program and service crash as a consequence.

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Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

Dreszer

Grochowski

Lewandowska

et al. 2020

Human Brain Mapping

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Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.

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Multivariate multiscale entropy: A tool for complexity analysis of multichannel data

Cited by 300 publications

References 38 publications

Dynamical complexity of human responses: a multivariate data-adaptive framework

Dynamical complexity of human responses: a multivariate data-adaptive framework

Program Aging and Service Crash

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

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