Abstract
ObjectiveEpilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.MethodsThe threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann–Whitney–Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE).ResultsThe results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions.ConclusionThe threshold symbolic entropy has provided improved accuracy in quantifying the dynamics of healthy and epileptic subjects during EC an EO conditions for each electrode compared to the MSE.
Highlights
Epilepsy is monitored using electroencephalography signals (EEGs) and epileptic seizure detection algorithms [1]
Various techniques have been developed for understanding the mechanism of epileptic disorders and epileptic seizure detection [10,11,12] based on time–frequency decomposition [13] and wavelet-based spare functional linear model [14]
The results show that the normalized corrected Shannon entropy (NCSE) value of the healthy EEG subjects is higher than that of the epileptic subjects as shown in Table 1 as well as the NCSE value of the EO condition than that of the EC condition during resting states in Table 2 at a smaller threshold range
Summary
Epilepsy is monitored using electroencephalography signals (EEGs) and epileptic seizure detection algorithms [1]. Various techniques have been developed for understanding the mechanism of epileptic disorders and epileptic seizure detection [10,11,12] based on time–frequency decomposition [13] and wavelet-based spare functional linear model [14]. Buteneers et al [16] used reservoir computing (RC) to detect epileptic seizure on intercranial rate data. Researchers have employed DWT-based ApEn and artificial neural network [19], probability distribution based on equal frequency discretization [20], and best basis wavelet functions in temporal lobe mimetic [21] for detection and analysis of EEG epileptic seizures
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