Factor models of the Kolmogorov–Sinai entropy indicators of EEG in adolescents with depression

t Introduction. According to the WHO, there is currently an increase in the prevalence, incidence and rejuvenation of depression. This phenomenon is also observed in adolescents. Purpose of the study. Search for sensitive and specific «markers» of depressive disorder in adolescents, which not only make it pos-sible to distinguish between patients and healthy people, but will also be able to assess the effectiveness of different types of treatment. The contingent of the surveyed. Research methods. Examined: 1. Group of adolescents with depression: 52 patients (35 girls and 17 boys). 2. Control group (healthy) — 40 adolescents (18 girls and 22 boys). 3. The EEG was recorded in a state of calm wakefulness and during mental stress. 4. EEG analysis — qEEG software complex — NeuroResearcher®InnovationSuite (MI&T Institute, Ukraine). The entropy of Kolmogorov–Sinai EEG was calculated — a nonlinear indicator of the state of neurodynamics in the studied EEG electrode placement. 5. Multivariate statistical analysis. Factor analysis was used to create the models (STATISTICA, 13.3). Results. The search for objective quantitative «markers» of the depressive state of both sexes adolescents was carried out on the basis of nonlinear EEG analysis and the creation of factor models of the results obtained. The factorial models of the Kolmogorov–Sinai EEG entropy of the studied areas of the cerebral hemispheres of sick and healthy both sexes adolescents in a state of calm wakefulness and during mental test were obtained. A physiological interpretation of the identified main factors is given. Comparison of factor models made it possible to identify differences between depressed and healthy adolescents, as well as gender differences. Differences in the factor models of the EEG pacemaker parameters were also revealed in depressed adolescents in a state of calm wakefulness and during mental stress. Based on the obtained factor models, it is possible to calculate the individual values of the factors for each pa-tient. This allows to determine the individual severity of the studied pathology. The revealed significant differences in factor models in adolescents of both sexes with depression in comparison with factor models of adolescents in the control group can be used to detect depressive disorder during EEG examination. Key words: Depression in adolescents; EEG; Nonlinear EEG analysis; Kolmogorov–Sinai entropy; Factor analysis.

Is EEG a Useful Examination Tool for Diagnosis of Epilepsy and Comorbid Psychiatric Disorders?

Diagnosis of epilepsy usually involves interviewing the patients and the individuals who witnessed the seizure. An electroencephalogram (EEG) adds useful information for the diagnosis of epilepsy when epileptic abnormalities emerge. EEG exhibits nonlinearity and weak stationarity. Thus, nonlinear EEG analysis may be useful for clinical application. We examined only about English language studies of nonlinear EEG analysis that compared normal EEG and interictal EEG and reported the accuracy. We identified 60 studies from the public data of Andrzejak 2001 and two studies that did not use the data of Andrzejak 2001. Comorbid psychiatric disorders in patients with epilepsy were not reported in nonlinear EEG analysis except for one case series of comorbid psychotic disorders. Using a variety of feature extraction methods and classifier methods, we concluded that the studies that used the data of Andrzejak 2001 played a valuable role in EEG diagnosis of epilepsy. In the future, according to the evolution of artificial intelligence, deep learning, new nonlinear analysis methods, and the EEG association with the rating scale of the quality of life and psychiatric symptoms, we anticipate that EEG diagnosis of epilepsy, seizures, and comorbid psychiatric disorders in patients with epilepsy will be possible.

NONLINEAR TIME SERIES ANALYSIS IN EPILEPSY

The framework of the theory of nonlinear dynamics provides powerful concepts and algorithms to study complicated dynamics such as brain electrical activity (electroencephalogram, EEG). Although different influencing factors render the use of nonlinear measures in a strict sense problematic, converging evidence from various investigations now indicates that nonlinear EEG analysis provides a means to reliably characterize different states of physiological and pathophysiological brain function. We here focus on applications of nonlinear EEG analysis in epileptology. Epilepsy affects more than 50 million individuals worldwide – approximately 1% of the world's population. The disease is characterized by a recurrent and sudden malfunction of the brain that is termed seizure. Nonlinear EEG analysis techniques allow to reliably identify the seizure generating structure (epileptic focus) in different areas of the brain even during seizure-free intervals, to disentangle complex spatio-temporal interactions between the epileptic focus and other areas of the brain, and to define a specific state predictive of an impending seizure. Nonlinear EEG analysis provides supplementary information about the epileptogenic process in humans, contributes to an improvement of the presurgical evaluation of epilepsy patients, and offers a basis for the development of new therapy concepts for seizure prevention.