20-38

UDC 621.391
DOI: 10.15350/2306-2819.2018.3.20

AMPLITUDE-FREQUENCY EEG MAPPING FOR CLINICIST SUPPORT
IN VISUAL EEG ANALYSIS

Ya. A. Furman¹, V. V. Sevastyanov^1,2, I. D. Stulin³, K. O. Ivanov¹
¹Volga State University of Technology,
3, Lenin Square, Yoshkar-Ola, 424000, Russian Federation
E-mail: krtmbs@volgatech.net
²Center for Speech Pathology and Neurorehabilitation of Neurosensor and Motor Disturbances,
65, Proletarskaya St., Yoshkar-Ola, 424031, Russian Federation
E-mail: cpr@mari-el.ru
³A.I. Evdokimov Moscow State University of Medicine and Dentistry of the Ministry of Health of the Russian Federation,
20, bldg 1, Delegatskaya Street, Moscow, 127473, Russian Federation
E-mail: stu-clinic@mail.ru

ABSTRACT

Introduction. Clinical neurophysiology has developed two approaches to solving diagnostic problems based on the results of EEG analysis. The first approach is based on a visual analysis, performed by an experienced clinician, and the second approach uses digital signal processing for the automatic EEG analysis on computers. When individual diagnostics is carried out, abilities of a man to solve such poorly formalized tasks as detection, recognition and the analysis of scenes under conditions of interference and nonstationarity of signals significantly exceed the capabilities of the EEG. From this point of view, the visual EEG methods remain in demand, but in order to increase their efficiency, they require preliminary digital processing that reduces the volume of routine operations with the analyzed EEG. The purpose of the article is to develop algorithms for the local EEG analysis that allow presenting data of EEG examinations in a familiar and understandable form, assisting in reaching a clinical conclusion. For this purpose, the following problems are solved in the work: 1) a review of the EEG analysis methods; 2) a review of the contour model of the EEG signal, developed by the authors that allows its decomposition and determination of informative parameters of the forms of EEG constituent elements; 3) a review of the EEG segmentation algorithm, which allows representing the signal in the form of an ordered sequence of waves, bounded by global-local minimum points; 4) substantiation of the structural approach for the analysis of EEG signals; 5) development of an algorithm for computing the frequency characteristics of an isolated segment of the EEG, represented by the contour model. Results. A structural approach to the analysis of the EEG curve, consisting in the fact that the recognizable image - an electroencephalogram is constructed by connecting simple subpatterns, called non-derivative elements, is proposed. Algorithms and mathematical models for estimating informative parameters of non-derivative EEG elements are developed. To support the clinician in the visual analysis of the EEG, an algorithm for estimating the frequency parameters of isolated EEG segments, presented in a contour form, has been developed. The practical value of the work lies in the fact that the mathematical apparatus, considered in the article allows performing a significant part of the "speculative" operations on the computer and presenting them as amplitude and frequency maps, containing the results of measurements of the analyzed oscillation. The proposed approach significantly reduces the amount of "speculative" operations, performed by the clinician and provides an opportunity to perform the intellectual part of the EEG analysis more objectively, qualitatively and quickly.

KEYWORDS

computer-based electroencephalography; central nervous system; elementary vector; elementary contour; electroencephalogram

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ACKNOWLEDGMENT

The work was carried out with financial support from the Ministry of Education and Science of the Russian Federation, project RFMEFI577170254 «A system of intraoperative navigation with the support of the augmented reality technology, based on virtual 3D models of organs, obtained using CT diagnostics, for minimally invasive operations».

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For citation: Furman Ya. A., Sevastyanov V. V., Stulin I. D., Ivanov K. O. Amplitude-Frequency EEG Mapping for Clinicist Support in Visual EEG Analysis. Vestnik of Volga State University of Technology. Ser.: Radio Engineering and Infocommunication Systems. 2018. No 3 (39). Pp. 20-38. DOI: 10.15350/2306-2819.2018.3.20