Time-varying time-frequency complexity measures for epileptic EEG data analysis

Objective: Our goal is to use existing and to propose new time-frequency entropy measures that objectively evaluate the improvement on epileptic patients after medication by studying their resting state EEG recordings. An increase in the complexity of the signals would confirm an improvement in the general state of the patient. Methods: We review the Rényi entropy based on time-frequency representations, along with its time-varying version. We also discuss the entropy based on singular value decomposition computed from a time-frequency representation, and introduce its corresponding time-dependant version. We test these quantities on synthetic data. Friedman tests are used to confirm the differences between signals (before and after proper medication). Principal component analysis is used for dimensional reduction prior to a simple threshold discrimination. Results: Experimental results show a consistent increase in complexity measures in the different regions of the brain. These findings suggest that extracted features can be used to monitor treatment. When combined, they are useful for classification purposes, with areas under ROC curves higher than 0.93 in some regions. Conclusion: Here we applied time-frequency complexity measures to resting state EEG signals from epileptic patients for the first time. We also introduced a new time-varying complexity measure. We showed that these features are able to evaluate the treatment of the patient, and to perform classification. Significance: The time-frequency complexities, and their time-varying versions, can be used to monitor the treatment of epileptic patients. They could be applied to a wider range of problems

A new approach to sample entropy of multi-channel signals: application to EEG signals

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

This paper introduces an entropy based method that measures complexity in non-stationary multivariate signals. This method, called Mutivariate Improved Weighted Multiscale Permutation Entropy (mvIWMPE), has two main advantages: (i) it shows lower variance for the results when applied on a wide range of multivariate signals; (ii) it has good accuracy quantifying complexity of different recorded states in signals and hence discriminating them. mvIWMPE is based on two previously introduced permutation entropy algorithms, Improved Multiscale Permutation Entropy (IMPE) and Multivariate Weighted Mul-tiscale Permutation Entropy (mvWMPE). It combines the concept of coarse graining from IMPE and the introduction of the weight of amplitudes of the signals from mvWMPE. mvIWMPE was validated on both synthetic and human electroencephalographic (EEG) signals. Several synthetic signals were simulated: mixtures of white Gaussian noise (WGN) and pink noise, chaotic and convergent Lorenz system signals, stochastic and deterministic signals. As for real signals, resting-state EEG recorded in healthy and epileptic children during eyes closed and eyes open sessions were analyzed. Our method was compared to multivariate multiscale, multivariate weighted multiscale and multivariate improved multiscale permutation entropy methods. Performance on synthetic as well as on EEG signals showed more undeviating results and higher ability for mvIWMPE discriminating different states of signals (chaotic vs convergent, WGN vs pink noise, stochastic vs deterministic simulated signals, and eyes open vs eyes closed EEG signals). We herein proposed an efficient method to measure the complexity of multivariate non-stationary signals. Experimental results showed the accuracy and the robustness (in terms of variance) of the method

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients

Multichannel time-frequency complexity measures for the analysis of age-related changes in neuromagnetic resting-state activity

We propose new multichannel time-frequency complexity measures to evaluate differences on magnetoencephalograpy (MEG) recordings between healthy young and old subjects at rest at different spatial scales. After reviewing the Renyi and singular value decomposition entropies based on time-frequency representations, we introduce multichannel generalizations, using multilinear singular value decomposition for one of them. We test these quantities on synthetic data, illustrating how the introduced complexity measures focus on number of components, nonstationarity and similarity across channels. Friedman tests are used to confirm the differences between young and old groups, and heterogeneity within groups. Experimental results show a consistent increase in complexity measures for the old group. When analyzing the topographical distribution of complexity values, we found clusters in the frontal sensors. The complexity measures here introduced seem to be a better indicator of the neurophysiologic changes of aging than power envelope connectivity. Here we applied new multichannel time-frequency complexity measures to resting-state MEG recordings from healthy young and old subjects. We showed that these features are able to reveal regional clusters. The multichannel time-frequency complexities can be used to monitor the aging of subjects. They also allow a mutual information approach, and could be applied to a wider range of problems

A new mutual information measure to estimate functional connectivity: preliminary study

International audienc

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

International audienceThis paper introduces an entropy based method that measures complexity in non-stationary multivariate signals. This method, called Mutivariate Improved Weighted Multiscale Permutation Entropy (mvIWMPE), has two main advantages: (i) it shows lower variance for the results when applied on a wide range of multivariate signals; (ii) it has good accuracy quantifying complexity of different recorded states in signals and hence discriminating them. mvIWMPE is based on two previously introduced permutation entropy algorithms, Improved Multiscale Permutation Entropy (IMPE) and Multivariate Weighted Mul-tiscale Permutation Entropy (mvWMPE). It combines the concept of coarse graining from IMPE and the introduction of the weight of amplitudes of the signals from mvWMPE. mvIWMPE was validated on both synthetic and human electroencephalographic (EEG) signals. Several synthetic signals were simulated: mixtures of white Gaussian noise (WGN) and pink noise, chaotic and convergent Lorenz system signals, stochastic and deterministic signals. As for real signals, resting-state EEG recorded in healthy and epileptic children during eyes closed and eyes open sessions were analyzed. Our method was compared to multivariate multiscale, multivariate weighted multiscale and multivariate improved multiscale permutation entropy methods. Performance on synthetic as well as on EEG signals showed more undeviating results and higher ability for mvIWMPE discriminating different states of signals (chaotic vs convergent, WGN vs pink noise, stochastic vs deterministic simulated signals, and eyes open vs eyes closed EEG signals). We herein proposed an efficient method to measure the complexity of multivariate non-stationary signals. Experimental results showed the accuracy and the robustness (in terms of variance) of the method

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

International audienceIn this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients