Comparative study of multiscale entropy analysis and symbolic time series analysis when applied to human gait dynamics

Qumar, Anees; Aziz, Wajid; Saeed, Sharjil; Ahmed, Imtiaz; Hussain, Lal

doi:10.1109/icosst.2013.6720618

Cited by 19 publications

(14 citation statements)

References 7 publications

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“…The entropy was measured for control animals and epileptic animals for individual channels and showed that control animals had larger entropy values than the epileptic animals, also consistent with previous studies that entropy values are reduced for biological signals associated with death and aging (17,30,31,(33)(34)(35)(36). Table 1 shows the MSE profile analysis where healthy subject set O (Eye open) and epileptic ictal subjects depict highly significant results at all time scales 1 to 25.…”

Section: Discussionsupporting

confidence: 67%

“…The epileptic animals had lower entropy values (31,32) in their EEG signals, shown previously in case of interictal states to pathological and diseased biological systems (30,31,(33)(34)(35)(36)(37)(38). 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).…”

Section: Discussionmentioning

confidence: 63%

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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: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 63%

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

et al. 2018

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“…Permutation entropy (PE) [23] and its improved algorithms [24,25] have been proposed as characteristics extraction in order to make a comprehensive analysis of biological and economical systems [26]. However, Qumar et al [27] concluded SyEn is a more statistically significant separation than MSE between normal and walking under various stress conditions. Moreover, SyEn has higher calculation efficiency on the whole for different time series data lengths.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, PE does not consider the influence of the difference between amplitude values for a given time series and takes a litter more computation time than SyEn [26,28]. Consequently, SyEn [7,8,27,29] can provide an accurate assessment of the dynamic behavior and takes less computation time for various signals.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Symbolic Entropy Analysis of Human Gait Signals

Cao

Liao

et al. 2017

Entropy

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Abstract:The complexity quantification of human gait time series has received considerable interest for wearable healthcare. Symbolic entropy is one of the most prevalent algorithms used to measure the complexity of a time series, but it fails to account for the multiple time scales and multi-channel statistical dependence inherent in such time series. To overcome this problem, multivariate multiscale symbolic entropy is proposed in this paper to distinguish the complexity of human gait signals in health and disease. The embedding dimension, time delay and quantization levels are appropriately designed to construct similarity of signals for calculating complexity of human gait. The proposed method can accurately detect healthy and pathologic group from realistic multivariate human gait time series on multiple scales. It strongly supports wearable healthcare with simplicity, robustness, and fast computation.

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“…They assert that this method could be demanded for implementation in real-time observation application such as structural health monitoring. In [32], the STSA is used in human gait dynamics. The results of this study can have implication modeling physiological control mechanism and for quantifying human gait dynamics in physiological and stressed conditions.…”

Section: Stsa In Applied Sciencesmentioning

confidence: 99%

Symbolic Time Series Analysis and Its Application in Social Sciences

Risso¹

2018

Time Series Analysis and Applications

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The present chapter intents to present the symbolic time series analysis (STSA) reviewing the recent developments in sciences. Even if there are very few works applied to social sciences, STSA has a potential to be developed. In particular, due to the limitations about historical data, fields such as Economics and Finance need to develop statistical tests to prove their hypotheses. An independence test and a causality test based on STSA are reviewed. They seem to be more powerful, detecting different kinds of nonlinearities compared with the classical tests, usually applied in social sciences. However, there is much work to do with STSA, and social sciences are a fertile field for the development of new powerful tools.

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Comparative study of multiscale entropy analysis and symbolic time series analysis when applied to human gait dynamics

Cited by 19 publications

References 7 publications

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

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

Multivariate Multiscale Symbolic Entropy Analysis of Human Gait Signals

Symbolic Time Series Analysis and Its Application in Social Sciences

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