A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data

Kuntzelman, Karl; Rhodes, L. Jack; Harrington, Lillian N.; Miskovic, Vladimir

doi:10.1016/j.bandc.2018.03.010

Cited by 30 publications

(22 citation statements)

References 70 publications

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“…Multiscale dispersion entropy (MDE) was used to quantify the complexity of EEG signals over numerous time scales (Azami, Rostaghi, Abásolo, & Escudero, 2017;Rostaghi & Azami, 2016). Dispersion entropy is related to sample and permutation entropy, but it is more tolerant to the presence of noise in time series signals and has a considerably shorter computation time (Rostaghi & Azami, 2016 (Kuntzelman, Rhodes, Harrington, & Miskovic, 2018). A schematic depicting some of the major steps in the calculation of dispersion entropy is provided in Figure 1 analogous to keeping the tolerance fixed for sample entropy calculations which, for physiological signals, is preferable to recalculating tolerance at each scale factor (Castiglioni, Coruzzi, Bini, Parati, & Faini, 2017).…”

Section: Multiscale Dispersion Entropy Analysesmentioning

confidence: 99%

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Miskovic

MacDonald

Rhodes

et al. 2018

Human Brain Mapping

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137

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We explored changes in multiscale brain signal complexity and power‐law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate‐based tests of nonlinearity we also examined whether any of the sleep stage‐dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time‐scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.

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Section: Multiscale Dispersion Entropy Analysesmentioning

confidence: 99%

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Miskovic

MacDonald

Rhodes

et al. 2018

Human Brain Mapping

Self Cite

164

137

View full text Add to dashboard Cite

show abstract

“…Thus, beyond bias controls noted above, we argue for rigorous statistical controls when 857 evaluating the shared and unique predictive utility of power and multiscale entropy in neural 858 time series data. Miskovic, 2018). The association between broadband signal entropy and spectral slopes 889 coheres with the notion that shallower slopes (i.e., more high frequency content) have a more 890 'noisy' or irregular appearance in the time domain.…”

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confidence: 69%

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

Kosciessa

Kloosterman

Garrett³

2019

Preprint

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AbstractMultiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via “similarity bounds” biases coarse MSE scales – that are thought to reflect slow dynamics – by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales – presumed to indicate fast dynamics – is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.Author SummaryBrain signals exhibit a wealth of dynamic patterns that that are thought to reflect ongoing neural computations. Multiscale sample entropy (MSE) intends to describe the temporal irregularity of such patterns at multiple time scales of brain function. However, the notion of time scales may often be unintuitive. In particular, traditional implementations of MSE are sensitive to slow fluctuations at fine time scales, and fast dynamics at coarse time scales. This conceptual divergence is often overlooked and may lead to difficulties in establishing the unique contribution of MSE to effects of interest over more established spectral power. Using simulations and empirical data, we highlight these issues and provide evidence for their relevance for valid practical inferences. We further highlight that standard MSE and traditional spectral power are highly collinear in our example. Finally, our analyses indicate that spectral filtering can be used to estimate temporal signal irregularity at matching and intuitive time scales. To guide future studies, we make multiple recommendations based on our observations. We believe that following these suggestions may advance our understanding of the unique contributions of neural signal irregularity to neural and cognitive function across the lifespan.

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“…Considering that MFE has been shown to be more closely correlated with MSE than with other multiscale entropy measures 19 , both MSE and MFE were analyzed in the current study. To determine the local oxygen saturation following thermal stimuli, the complexity of the oxygen saturation signals was analyzed using MSE and MFE.…”

Section: Complexity Calculationmentioning

confidence: 99%

“…Multiscale fuzzy entropy (MFE), as an enhanced MSE method, is also an effective method for evaluating the complexity of time series. Relevant research shows that MFE has a more significant correlation with MSE than other multiscale entropies 19 .…”

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confidence: 99%

Impact of local thermal stimulation on the correlation between oxygen saturation and speed-resolved blood perfusion

Wang

Jia

Liu

et al. 2020

Sci Rep

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The physiologically important relationship between oxygen saturation and blood flow is not entirely understood, particularly with regard to the multiple velocity components of flow and temperature. While our previous studies used classic laser Doppler flowmetry combined with an enhanced perfusion probe to assess local blood flow following thermal stimulation, oxygen saturation signals were not assessed. Thus, the current study used multiscale entropy (MSE) and multiscale fuzzy entropy (MFE) to measure the complexity of oxygen saturation signals following thermal stimulation in healthy subjects. The results indicate that thermal stimulation increases oxygen saturation and affects the measured signal complexity in a temperature-dependent fashion. Furthermore, stimulus temperature not only affects the correlation between speed-resolved blood perfusion and oxygen saturation, but also the correlation between the complexity area indices (CAI) of the two signals. These results reflect the complexity of local regulation and adaptation processes in response to stimuli at different temperatures. While the classic technique laser Doppler flowmetry (LDF) has been frequently used to measure blood flow in tissues, it cannot clearly differentiate between different vascular compartments. Recently, a multiparameter model based on the Monte Carlo algorithm has made it possible to more precisely resolve the different velocity components in microcirculatory perfusion 1-4. By integrating diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF) into a single fiber-optic probe, the Enhanced Perfusion and Oxygen Saturation (EPOS) system can measure blood flow and blood oxygen saturation simultaneously 5. This new method may provide further insight into vascular dysfunction at both local and systemic levels 6,7. Oxygen saturation arises from a dynamic balance between O 2 supply and consumption in capillary, arteriolar, and venular beds and it is generally believed that local oxygen saturation is closely related to blood perfusion 8. Previous studies have demonstrated an exponential correlation between oxygen saturation and blood flow. When the speed of blood flow increases, the capillary transit time decreases, resulting in an instantaneous decrease in oxygen extraction and an increase in the overall level of oxygen saturation assuming oxygen consumption remains constant 9. However, while blood flow can be resolved into different components according to speed, the relationship between oxygen saturation and speed-resolved blood perfusion is still unclear. Building upon our previous study 10 , which measured speed-resolved blood perfusion using the EPOS system, the present study combined these measurements with a simultaneous analysis of the changes in the complexity of oxygen saturation signals in response to thermal stimuli to clarify the relationship between oxygen saturation and speed-resolved blood perfusion. As the regulation of the circulatory system is known to be a nonlinear process 11 , sample entropy and multisca...

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A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data

Cited by 30 publications

References 70 publications

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

Impact of local thermal stimulation on the correlation between oxygen saturation and speed-resolved blood perfusion

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