Complexity Analysis of Eeg Motor Movement With Eye Open and Close Subjects Using Multiscale Sample Entropy (Mse) Technique

Hussain, Lal; Anjum, Sadia; Aziz, Wajid; Abbasi, Mohsin Manshad

doi:10.21506/j.ponte.2016.7.26

Cited by 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: 85%

“…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: Discussionsupporting

confidence: 66%

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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: 85%

Section: Discussionsupporting

confidence: 66%

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

et al. 2018

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“…As reflected in Table 3, by employing Multiscale sample entropy (MSE) and extracting morphological features, the NSCLC (0.4050) exhibit higher complexity than SCLC (0.3397). Similarly, by employing MSE [81]- [85] and [39], [44], [45], [48].…”

Section: Resultsmentioning

confidence: 99%

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

et al. 2019

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Lung cancer is the major cause of cancer-related deaths worldwide with poor survival due to the poor diagnostic system at the advanced cancer stage. In the past, researchers developed computeraided diagnosis (CAD) systems, which were greatly used by the radiologist for identifying the abnormalities and applied few features extracting methods. The physiology and behavior of various physiological systems can be best investigated using nonlinear dynamical measures for capturing the intrinsic dynamics, which is influenced due to multiple pathologies by the degradation of structural and functional components. As cancer images contain hidden information, which can be best analyzed using these dynamical measures. In this paper, we proposed multiscale sample entropy (MSE) with a mean and KD-tree algorithmic approach, multiscale permutation entropy (MPE), multiscale fuzzy entropy (MFE), and refined composite multiscale fuzzy entropy (RCMFE) with mean, variance, and standard deviation. The statistically significant results were computed to distinguish non-small-cell lung cancer (NSCLC) from SCLC by extracting morphological, texture, and elliptic Fourier descriptors (EFDs). The highest significant results obtained based on texture features using MFE with standard deviation give the P-value of 1.95E-50, morphological features using RCMFE with mean provide the P-value of 3.01E-14, and EFDs features using MFE with variance give the P-value of 1.04E-13. The results reveal that the improved complexity measures based on refined fuzzy entropy outperformed in analyzing the dynamics of lung cancer and will provide a new insight into extract meaningful hidden information present in the Lung cancer images, which will be very helpful to further distinguish NSCLC and SCLC for early diagnosis and prognosis. II. MATERIAL AND METHODSA. DATA SET

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“…Nevertheless, the class of statistical measures is limited to implementing the algorithm in Equation (5) due to the estimated relevance set fails for normality and homoscedasticity, applying Kolmogorov-Smirnov and Bartlett's tests, respectively. Instead, we validated whether two labeled samples are likely to derive from the same population, using the nonparametric Mann-Whitney test that is often conducted on EEG connectivity (Hussain et al, 2017). However, we could not have expected the connectivity values to be uncorrelated between different piecewise intervals.…”

Section: Piecewise Construction Of Group-level Connectivitymentioning

confidence: 99%

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

et al. 2020

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Complexity Analysis of Eeg Motor Movement With Eye Open and Close Subjects Using Multiscale Sample Entropy (Mse) Technique

Cited by 7 publications

References 0 publications

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

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

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

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