Identification and analysis of statistical patterns in human electroencephalogram signals at different degrees of obsessive-compulsive disorder

Abstract

Obsessive-compulsive disorder (OCD) is a mental disorder characterized by compulsive states (obsessions) from which the patient attempts to escape through repetitive actions (compulsions). This disorder has different manifestations, and more often has a chronic character, interspersed with periods of absence of symptoms. At the present time, methodological approaches that have developed historically are used for the detection of OCD, but with different diagnostic criteria, which does not allow collecting them into a unified system and sufficiently studying the clinical dynamics. As a rule, such criteria contain information on the duration of OCD symptoms over a certain time period, decrease in quality of life, level of psychological discomfort, resistance to symptoms and control over them. For example, the Yale-Brown scale includes 10 points: 5 to assess the degree of obsessions and 5 for compulsions. Each of the points is evaluated on a 5-point system. The summary score determines the degree of OCD severity. The disadvantage of this approach is that it does not take into account the varieties of obsessions and compulsions in the final result of the assessment. Except for the fact that it is difficult to distinguish OCD symptoms from the brain disorders arising in other mental disorders, it is also difficult to make a distinction. Thus, hopes in solving the problems of diagnosing OCD, first of all, and increasing the objectivity of the diagnoses are connected with widely used in psychiatry clinical electroencephalographic (EEG) studies and corresponding methods of processing of digitized EEG signals.
In this paper, based on the author's methods of analyzing time signals: memory functions formalism and flicker-noise spectroscopy, EEG signals of 30 subjects with different levels of OCD symptoms severity are investigated. The search for diagnostic patterns is performed in two directions: the study of bioelectrical activity of individual cortical areas of the brain in order to identify those in which the most significant functional changes are observed; the study of the effects of synchronization and coherence of signals generated by different cortical areas of the human brain. The results obtained will be of interest for computational biophysics, physics of living systems, evolutionary psychology, and psychiatry.

Speaker

Alexander A. Elenev
Institute of Physics, Kazan Federal University, Kazan, Russia
Russian Federation

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