Tychkov Aleksandr Yur'evich, Candidate of engineering sciences, deputy director of the Research Institute of Fundamental and Applied Studies, Penza State University (40 Krasnaya street, Penza, Russia), firstname.lastname@example.org
Churakov Petr Pavlovich, Doctor of engineering sciences, professor, sub-department of information-measuring equipment and metrology, Penza State University (40 Krasnaya street, Penza, Russia), email@example.com
Tychkova Anna Nikolaevna, Head of innovation analytical department, Penza State University (40 Krasnaya street, Penza, Russia), firstname.lastname@example.org
Background. The research object of is a device for electroencephalographic signals’ (EES) informative parameters measurement in respondents of extreme professions in conditions of an increased noise level (in complex extreme conditions). The research subject is the EES processing algorithms that improve the speed and accuracy of measurement results. The objective of the research is to carry out an analytical study of the known EES measuring devices, to develop a block diagram of the measuring device of the EES informative parameters and the EES processing algorithm in conditions of a increased noise level.
Materials and methods. To develop the block diagram of the measuring device of the EES informative parameters and the EES processing algorithm in conditions of an increased noise level during free motor activity of respondents the authors used techniques of digital signal processing (amplitude-frequency processing, bandpass filtering) and the basics of circuit design of measuring devices.
Results. An analysis of the known analytical solutions for the EES measuring informative parameters was done. The article shows development prospects of new solutions in development of the theory of digital signal processing for medical diagnosis purposes. The novel block diagram of the EES informative parameters measuring device and the EES processing algorithm in conditions of an increased noise level during free motor activity of respondents have been developed. A distinctive feature of the proposed solution is the algorithm has been supplemented with new blocks of analysis and comparison of EES with the norm and abnormality, as well as with block of suppression of the common mode noise, due to band-pass filter usage.
Conclusions. The proposed measuring device of the EES informative parameters in the biomedical practice, in contrast to the existing analog devices, can improve performance and efficiency of device more than 2 times owing to the use of the new EES processing algorithm in conditions of an increased noise level during free motor
activity of respondents. A distinctive feature of the algorithm is that the analysis of the EES frequency components is conducted in real-time with an automatic comparative analysis of frequency threshold value of theta, alpha and gamma waves per second.
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