Time Series Analysis Using Composite Multiscale Entropy

Wu, Shuen De; Wu, Chiu Wen; Lin, Shiou-Gwo; Wang, Chun Chieh; Lee, Kung‐Yen

doi:10.3390/e15031069

Cited by 281 publications

(225 citation statements)

References 21 publications

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“…Lower entropy values also reveal that the signal has reduced complexity and previous findings also show that the brain was reflected due to abnormal behaviour in the rats (12,20,24,32,38,39). In clinical context, it could be expected that this in coordination could be reflected in both psychiatric and cognitive dysfunction as observed in patients with MTLE (40,41).…”

Section: Discussionmentioning

confidence: 96%

“…The MSE is already used in different fields to analyse different problems, such as human gait dynamics, time series of river flow, heart rate variability, electro seismic time series, social dynamics, time series of traffic flow, chatter to milling process, and vibration of a vehicle etc. These works demonstrated the effective use of MSE algorithm to analyse these complex time series (20).…”

Section: Sample Entropymentioning

confidence: 99%

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Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

et al. 2018

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Objectives: The most common chronic disorder due to sudden change in the electrical activity of the brain is known as epilepsy. It causes millions of deaths every year and is the second major disorder after stroke. The epileptic process involves an abnormal synchronized firing of neurons usually characterized by recurrent seizures, which are highly complex, nonlinear and non-stationary in nature. Even between seizures, the epileptic brain is different from normal and pathological conditions. The classical methods fail to analyse the full dynamics in detecting epileptic seizure. The aim of this research was to quantify the dynamics of EEG signals between seizures and seizure-free intervals using entropy based complexity measures at multiple temporal scales. The complexity of epileptic seizure intervals is reduced because of degradation of structural and functional components. Thus, complexity measures are more robust to fully analyse the dynamics of these signals. Methods:The publicly available data comprise of three different groups of EEG signals: 1, Healthy subjects; 2, Epileptic subjects during seizure-free intervals (interictal EEG); and 3, Epileptic subjects during seizure (ictal EEG); each of 100 EEG channel sample at 174 Hz were taken to quantify the dynamics in these signals. To analyse the improved understanding of the epileptic process, complexity-based techniques of Multiscale Sample Entropy (MSE) and Wavelet Entropy (Wentropy) including Shannon, log energy, and threshold, and sure and norm developed in Matlab 2015a, were employed to distinguish these conditions. Mann-WhitneyWilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. Moreover, the area under the curve (AUC) was computed by developing multi-receiver operating curve (ROC) in Matlab 2015a to find the maximum separation to distinguish these conditions. Results: The complexity of healthy and epileptic subjects (including both in the presence of seizures and without seizure) was computed using MSE and Wentropy at multiple temporal scales. The healthy subjects exhibited higher complexity than the epileptic subjects. Likewise, the complexity of ictal (seizure subjects) was higher than the interictal (without seizures). To distinguish healthy subjects (Set O) from epileptic (Set S) subjects, the highest separation was obtained using wavelet norm 1.1 (AUC = 0.999) followed by wavelet Shannon (AUC = 0.9944), MSE (AUC = 0.9727) and wavelet threshold (AUC = 0.942). Conclusions: Optimal results using MSE were obtained at smaller scales whereas the wavelet entropies gave optimal results mostly at higher temporal scales. Moreover, the highest separation in form of AUC was obtained using the Wentropy method with norm parameter 1.1 to distinguish healthy (eye open) and epileptic seizure (ictal state) subjects followed by wavelet Shannon and MSE.

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Section: Discussionmentioning

confidence: 96%

Section: Sample Entropymentioning

confidence: 99%

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

et al. 2018

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“…Many modified versions of MSE have been developed to describe the multiscale properties of time series [32], such as composite multiscale entropy [33] and time-shift multiscale entropy (TSME) [34]. TSME was proposed by Pham [34], modifying the MSE over time series X of length N, with multiple time shifts at a given time interval kmax.…”

Section: Multiscale Entropy (Mse) and Time-shift Multiscale Entropy (mentioning

confidence: 99%

Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

Wang

Peng

et al. 2017

Entropy

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Abstract:In the diagnosis of neurological diseases and assessment of brain function, entropy measures for quantifying electroencephalogram (EEG) signals are attracting ever-increasing attention worldwide. However, some entropy measures, such as approximate entropy (ApEn), sample entropy (SpEn), multiscale entropy and so on, imply high computational costs because their computations are based on hundreds of data points. In this paper, we propose an effective and practical method to accelerate the computation of these entropy measures by exploiting vectors with dissimilarity (VDS). By means of the VDS decision, distance calculations of most dissimilar vectors can be avoided during computation. The experimental results show that, compared with the conventional method, the proposed VDS method enables a reduction of the average computation time of SpEn in random signals and EEG signals by 78.5% and 78.9%, respectively. The computation times are consistently reduced by about 80.1~82.8% for five kinds of EEG signals of different lengths. The experiments further demonstrate the use of the VDS method not only to accelerate the computation of SpEn in electromyography and electrocardiogram signals but also to accelerate the computations of time-shift multiscale entropy and ApEn in EEG signals. All results indicate that the VDS method is a powerful strategy for accelerating the computation of entropy measures and has promising application potential in the field of biomedical informatics.

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“…We observe that for τ = 1, the original sequence is recovered, whereas for τ > 1 the length of the new sequences is reduced to N/τ. We note that for each scale factor τ, there are τ coarse-grained sequences derived from the original one, as it was recently pointed out in the composite MSE [31,32]. Next, to complete the MSE steps the ApEn algorithm is applied to the sequence y τ k,j for each scale to evaluate the regularity/irregularity in the new block-sequences.…”

Section: Multiscale Entropy Analysis Of Textsmentioning

confidence: 99%

Evaluating the Irregularity of Natural Languages

Hernández-Gómez

Basurto-Flores

Obregón-Quintana

et al. 2017

Entropy

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Abstract:In the present work, we quantify the irregularity of different European languages belonging to four linguistic families (Romance, Germanic, Uralic and Slavic) and an artificial language (Esperanto). We modified a well-known method to calculate the approximate and sample entropy of written texts. We find differences in the degree of irregularity between the families and our method, which is based on the search of regularities in a sequence of symbols, and consistently distinguishes between natural and synthetic randomized texts. Moreover, we extended our study to the case where multiple scales are accounted for, such as the multiscale entropy analysis. Our results revealed that real texts have non-trivial structure compared to the ones obtained from randomization procedures.

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Time Series Analysis Using Composite Multiscale Entropy

Cited by 281 publications

References 21 publications

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

Evaluating the Irregularity of Natural Languages

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