Analysis of the Complexity Measures in the EEG of Schizophrenia Patients

Abstract

Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

“…(Color figure online) Cogn Neurodyn (2017) 11:147-160 155 three studies are in accordance with our predictions obtained by PE or OI measures, which demonstrate that the resting EEG/MEG activities in PD patients present an overall loss of complexity compared to controls. Such predictions also provide further support for the proposal that a loss of complexity appears when the nervous system becomes functionally impaired (Stam et al 1994(Stam et al , 1995Akar et al 2015Akar et al , 2016Chenxi et al 2016;Gao et al 2011;Darbin et al 2013;Müller et al 2001;Keller et al, 2014). In addition to above literatures, there have also been studies on the complexity of EEG rhythms in different subbands.…”

“…The recorded EEG waves have been shown to reflect the functional state of cortical layers and their subcortical driving structures, especially the cortical dysfunction of some etiologies (Freeman and Quiroga 2013;Akar et al 2015Akar et al , 2016Bowman et al 2016;Keller et al 2014). In clinical and basic research, EEG has been widely used to investigate the potential markers of neuropsychiatric disorders, including PD, schizophrenia, autism, major depressive disorder, Alzheimer's disease, epilepsy, and so on (Miller and O'Callaghan 2015;Han et al 2013;Gandal et al 2012;Klassen et al 2011;Hampel et al 2010;Kheiri et al 2012;Leuchter et al 2009).…”

“…Further, nonlinear analysis of brain activity has also revealed reliable information about the underlying dynamics related to cognitive process and brain pathology (Akar et al 2015(Akar et al , 2016Chenxi et al 2016;Bhattacharya and Lee 2016;Gao et al 2011). One common method was to characterize the complexity of the recorded neurosignals, since it carried important information about the cortical or subcortical dynamics (Akar et al 2015(Akar et al , 2016Chenxi et al 2016).…”

“…One common method was to characterize the complexity of the recorded neurosignals, since it carried important information about the cortical or subcortical dynamics (Akar et al 2015(Akar et al , 2016Chenxi et al 2016). Such nonlinear methods were different from spectral analysis, since they belonged to information theory or dynamical system theory (such as, chaos or fractal).…”

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

“…In the last decade, numerous methodologies or methods have been introduced in the literature for identifying several medical conditions such as diabetes [1,2], glaucoma [3,4], breast cancer [5,6], emotion detection [7], among others. Also, some proposals have been focused on the detection of several neurological disorders such as Alzheimer's disease (AD) [8][9][10][11], mild cognitive impairment (MCI) [12][13][14][15], delirium [16], epilepsy [17][18][19], sleep disorder [20][21][22], Parkinson's disease [23,24], autism spectrum disorder [25], schizophrenia [26,27], among others. These methods have been demonstrated to be efficient to diagnose a disease when it has developed symptoms in the human body.…”