Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states.
OBJECTIVE
Epilepsy 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.


METHODS
The 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).


RESULTS
The 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.


CONCLUSIONS
The 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.
University of Azad Jammu and Kashmir, Directorate of Quality Enhancement Cell, City Campus, Muzaffarabad, 13100, Azad Kashmir, Pakistan. lall_hussain2008@live.com.
Lal
http://orcid.org/0000-0003-1103-4938
L
Hussain
Department of Computer Science, Faculty of Computing and IT, University of Jeddah, Jeddah, Kingdom of Saudi Arabia.|Department of CS & IT, The University of Azad Jammu & Kashmir, City Campus, Muzaffarabad, Azad Kashmir, Pakistan.
Wajid
W
Aziz
Department of Computer Science, Faculty of Computing and IT, University of Jeddah, Jeddah, Kingdom of Saudi Arabia.
Jalal S
JS
Alowibdi
Department of Sociology, The University of Azad Jammu & Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
Nazneen
N
Habib
Department of Physics, The University of Azad Jammu & Kashmir, Chehla Campus, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
Muhammad
M
Rafique
Department of CS & IT, The University of Azad Jammu & Kashmir, City Campus, Muzaffarabad, Azad Kashmir, Pakistan.
Sharjil
S
Saeed
Department of CS & IT, The University of Azad Jammu & Kashmir, City Campus, Muzaffarabad, Azad Kashmir, Pakistan.
Syed Zaki Hassan
SZ
Kazmi
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D016022:Case-Control Studies; D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D009799:Ocular Physiological Phenomena; D012146:Rest / Q000502:physiology*
Brain activities; Epileptic seizure; Interictal and ictal seizure states; Normalized corrected Shannon entropy; Physiological complexity; Resting states; Symbolic dynamics