The multiscale entropy: Guidelines for use and interpretation in brain signal analysis

Courtiol, Julie; Perdikis, D.; Petkoski, Spase; Müller, Viktor; Huys, Raoul; Sleimen-Malkoun, Rita; Jirsa, Viktor K.

doi:10.1016/j.jneumeth.2016.09.004

Cited by 92 publications

(123 citation statements)

References 44 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…This suggests that the presence of broadband activity of EEGs may be needed for a comprehensive evaluation of complexity with multiscale entropy-based methods. Furthermore, we have related these findings with a very recent article providing guidelines on the interpretation of MSE results of brain signals [46] and showed that the profile of multivariate multiscale entropy of EEG signals at different frequency bands is not determined by the band-pass filtering process in comparison with the univariate multiscale entropy.…”

Section: Global Evaluation Of Multivariate and Univariate Multiscale mentioning

confidence: 59%

“…In the light of a recently published article providing guidelines on the interpretation of MSE µ results of brain signals [46], we evaluated all MSE and mvMSE methods on 40 different univariate and uncorrelated multivariate WGN time series band-pass filtered at 1-40 Hz, 4-8 Hz, 8-13 Hz, and 13-30 Hz, to investigate whether the entropy profiles of brain signals are linked to their power content. The length of the time series and the number of channels of the filtered multivariate WGN were respectively 1280 sample points (equal to the length of the EEG time series) and 16 (equal to the number of channels of EEG time series), and the parameter values for the multiscale methods were equal to those used for the EEG dataset.…”

Section: Global Evaluation Of Multivariate and Univariate Multiscale mentioning

confidence: 99%

See 1 more Smart Citation

Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Azami

Abásolo

Simons

et al. 2017

Entropy

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSE σ 2 ) to multichannel signals, termed multivariate MSE σ 2 (mvMSE σ 2 ), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSE σ 2 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40 Hz, θ, α, and β bands, and compare it with the previously-proposed multiscale entropy based on mean (MSE µ ), multivariate MSE µ (mvMSE µ ), and MSE σ 2 , to distinguish different kinds of dynamical properties of the spread and the mean in the signals. Results from 11 AD patients and 11 age-matched controls suggest that the presence of broadband activity of EEGs is required for a proper evaluation of complexity. MSE σ 2 and mvMSE σ 2 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSE µ and mvMSE µ ones, suggesting that the variance-based MSE and mvMSE can characterise changes in EEGs as a result of AD in a more detailed way. The p-values for the slope values of the mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques.

show abstract

Section: Global Evaluation Of Multivariate and Univariate Multiscale mentioning

confidence: 59%

Section: Global Evaluation Of Multivariate and Univariate Multiscale mentioning

confidence: 99%

Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Azami

Abásolo

Simons

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…The research in this context has been driven by the work of Costa et al [5], who introduced multiscale entropy (MSE) as a measure of the complexity of a time series evaluated as a function of the time scale at which the series is observed. Since its introduction, MSE has been successfully employed in several fields of science [6], becoming a prevailing method to quantify the complexity of biomedical time series [7,8] and gaining particular popularity in the analysis of brain [9,10] and cardiovascular variability [11,12] signals.…”

Section: Introductionmentioning

confidence: 99%

Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

et al. 2017

View full text Add to dashboard Cite

The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR) stochastic processes. The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the complexity of the process. The resulting linear MSE (LMSE) measure is first tested in simulations, both theoretically to relate the multiscale complexity of AR processes to their dynamical properties and over short process realizations to assess its computational reliability in comparison with RMSE. Then, it is applied to the time series of heart period, arterial pressure, and respiration measured for healthy subjects monitored in resting conditions and during physiological stress. This application to short-term cardiovascular variability documents that LMSE can describe better than RMSE the activity of physiological mechanisms producing biological oscillations at different temporal scales.

show abstract

“…Previous studies have linked the signals with high frequencies to information processing in local region whereas those with low frequencies to long-range communication between brain regions [46,130]. Together with the previous findings that the entropy of brain signals reflects the information processing in the brain, it is possible that MSE at different temporal scales may reveal the brain dynamics with different spatial scales [129]. Power spectral analyses in chronic pain patients at rest suggests an increased EEG power in lower frequency bands, including theta and alpha bands [131,132].…”

Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 80%

“…A known fact is that there is a close relationship between the temporal scales in MSE and the frequencies of the signals: the small scale (fine scales) factors reflect the complexity of oscillations at higher frequencies whereas larger scale (coarse scales) factors reflect those at lower frequencies [43,46,129]. Recently, Courtiol et al [129] have used simulated white noises and experimental EEG data to investigate how the values of MSE across different scales change along with different power spectra of the signals. The procedure of temporal averaging in MSE can be regarded as performing downsampling or low-pass filtering on the original signals.…”

Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 99%

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Low¹,

Kuo

Liu³

et al. 2017

Entropy

View full text Add to dashboard Cite

How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.

show abstract

The multiscale entropy: Guidelines for use and interpretation in brain signal analysis

Cited by 92 publications

References 44 publications

Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Contact Info

Product

Resources

About