Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

Hussain, Lal; Aziz, Wajid; Saeed, Sharjil; Shah, Sajjad Ali; Nadeem, Malik Sajjad Ahmed; Awan, Imtiaz Ahmed; Abbas, Ali Kareem; Majid, Abdul; Kazmi, Syed Zaki Hassan

doi:10.1515/bmt-2017-0041

Cited by 23 publications

(22 citation statements)

References 42 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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“…5a, b and a-d shows that epileptic mean features values of epileptic subjects are much greater than the healthy subjects for each case (a-c) because epileptic subjects are produced due to higher neurological chronic disorder and higher spikes are produced. The epileptic feature values of healthy subjects are higher than the epileptic subjects because healthy subjects exhibit higher complexity than the epileptic subjects which is also consistent with the previous studies (Hussain et al 2017b;Costa et al 2002).…”

Section: Overall Accuracy (Oa)supporting

confidence: 91%

“…In this study, we also have extracted the features based on time domain, frequency domain, complexity based measures and wavelet entropy methods for classifying the epileptic seizure subjects from that of healthy subjects and postictal heart rate oscillations. Apart, in this work, we extracted nonlinear features using sample entropy based on KD tree algorithmic approach (fast Sample entropy) and approximate entropy which gives outer performance than results obtained by Wang et al (2017) and are consistent with the results obtained by Hussain et al (2017b). Recently, Hussain et al (2017b) and Pan et al (2011) employed fast MSE which gives statistically more effective results than traditional MSE with reduced computational and memory complexity.…”

Section: Features Extractionsupporting

confidence: 72%

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Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

Hussain

2018

Cogn Neurodyn

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Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

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Extracting mass concentration time series features for classification of indoor and outdoor atmospheric particulates

et al. 2020

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Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

Cited by 23 publications

References 42 publications

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

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

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

Extracting mass concentration time series features for classification of indoor and outdoor atmospheric particulates

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