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

Abstract

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.

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

“…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].…”

“…Moreover, the complex dynamics of various physiological systems such as arrhythmia detection, epileptic seizure detection, Alzheimer disease, Alcoholism etc. [21], [40], [39], [44]- [49] have been studied. In this study, we first extracted morphological, texture and EFDs features and then employ the complexity measures to quantify the dynamics of lung cancer imaging data.…”

“…represents extreme PE value. MPE incorporates two procedures [80] First, a coarsegraining process detailed in [44] is applied to a time series as reflected in the Figure 1. For a given time series (a 1 , a 2 , a 3 .…”

Automatic Diagnosis of Epileptic Seizure in Electroencephalography Signals Using Nonlinear Dynamics Features

“…During epileptic seizure, a massive group of neurons in the cerebral cortex suddenly begin to discharge in a highly organized rhythmic pattern [28]. This rhythmic pattern causes a decreased entropy value during epileptic seizures, which indicates a reduction from intra-cortical information flow because epileptic seizures are emergent synchronous states whose dimensionality is reduced compared to non-epileptic activity [29]. Thus, the nonlinearity and the dynamic nature of the EEG signals can be evaluated efficiently using entropy-based features, and the proposed feature extraction method with a consideration of entropy changes could effectively help to improve the performance of epileptic seizure diagnosis.…”

“…Thus, the nonlinearity and the dynamic nature of the EEG signals can be evaluated efficiently using entropy-based features, and the proposed feature extraction method with a consideration of entropy changes could effectively help to improve the performance of epileptic seizure diagnosis. Entropy-related variants have been widely investigated to discriminate abnormal events of different disease types based on biomedical signals [29], [30]. However, few studies have investigated the performance of entropy ensembles used for seizure detection since each type of entropy measures only represents a specific aspect of signal information.…”