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Issue 3 (189), article 5

DOI:https://doi.org/10.15407/kvt189.03.079

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Shvets A.V.1, Dr (Medicine), Senior Researcher,
Head of Research Department of Special Medicine and Psychophysiology of Research Institute of Military Medicine of Ukrainian Military Medical Academy
e-mail: shvetsandro@gmail.com
Kich A.Y.2, PhD (Medicine),
Head of Military Medical Clinical Center of Occupational Pathology
e-mail: kikh76@ukr.net
1Research Institute of Military Medicine of Ukrainian Military Medical Academy,
04655, Ukraine, Kiev, Melnikova Str. 24
2Military Medical Clinical Center of Occupational Pathology of Servicemen
of Ukrainian Armed Forces, 08203, Ukraine, Kyiv region, Irpin. 11-line Str. 1.

THE DECISION SUPPORT MODEL FOR FORE-CASTING OF WOUNDED AND SICK RESTORATION IN HOSPITAL CONDITIONS BASED ON PSYCHOPHYSIOLOGICAL DATA

Introduction. The psychological unpreparedness, non-coping fear with the responsibilities, feeling guilt to the dead, striving to survive in terms of destruction and deaths of others, extreme strain of duty, violations of food recreation and other harmful factors of duty undoubtedly reduce the human adaptive reserves and lead to non-constructive changes of behaviors and disadaptation syndrome that need their assessment for further rehabilitation treatment requirement.
The purpose of the study is to elaborate the decision support model for medical recovery assessment by estimation of functional state of wounded and sick persons during their treatment in hospital conditions to substantiate the necessity of a further rehabilitation.
Materials and methods. There were selected two groups of 25–45 ages’ men: I group — 30 persons that got mild traumatic brain injury (mTBI) during the 2014–2015 years and had comorbid somatic pathology, the II group — 30 people who had only therapeutic pathology. The assessment of functional state (FS) was based on heart rate variability (HRV) and electroencephalography (EEG) data before and after their rehabilitation treatment.
Results. The features of patients recovering based on the study of EEG and HRV characteristics were significantly worse according to the functional state (FS) of individuals that had mTBI (only 23,3 % of positive dynamics) comparing with others (83,4 %;
p < 0,001). There were described structural features of three types of EEG phenomena, which occur in patients with mTBI. The analysis of interrelations of EEG and HRV data additionally confirms a slow recovery of FS of patients with mTBI. The physiological value of FS regulation was the highest among individuals that had mTBI.
Conclusions. The decision support model for assessment of human recovery potential by evaluation of functional state of wounded and sick persons allows quantitatively predict the need for further rehabilitation after the hospital treatment. It was shown that application of EEG and HRV hardware during rehabilitation of combatants in hospital conditions allows to evaluate a specific morphological defects and the degree of human rehabilitation potential.
Keywords: rehabilitation potential, participants in anti-terrorist operations, functional state, heart rate variability, electroencephalography

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REFERENCES

1 Gorgo Yu.P., Malikov M.V., Bogdanovska N.V. Assessment and management of functional states: A manual for students in higher education. Zaporozhye: National University, 2005. p. 135 [in Ukrainian].

2 Il’in E.P. Psychophysiology of Human State. St. Petersburg: Piter, 2005. p. 412. [in Russian].

3 Aldonin G.M., Zheludko S.P. Index of effective correction of an organism functional state. Journal of Siberian Federal University. Engineering & Technologies. 2009. (3). P. 311–317 [in Russian].

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5 Korzheletsky O.S., Jura N.O., Kazarova S.V. Functional and pharmacological tests, the feasibility of a differential diagnosis of ECG changes of organic and functional origin in terms of multi-hospital. Ukraine. Health of the Nation. 2013. 27 (3). P. 54–59 [in Ukrainian].

6 Mayorov O.Yu., Fenchenko V.N. Study of brain bioelectrical activity from the standpoint of multidimensional linear and nonlinear EEG analysis. Clinical Informatics and Telemedicine. 2008. 5 (4). P. 12–20 [in Russian].

7 Zhirmunskaya E.A. The bioelectrical activity of healthy and sick human brain. In the book: Physiology Guide. Clinical Physiology. Leningrad: Nauka, 1972. p. 313 [in Russian].

8 Shu I.W., Onton J.A., O’connell R.M., Simmons A.N., Matthews S.C. Combat veterans with comorbid PTSD and mild TBI exhibit a greater inhibitory processing ERP from the dorsal anterior cingulate cortex. Psychiatry Res. 2014. 224 (1). P. 58–66.
https://doi.org/10.1016/j.pscychresns.2014.07.010

9 Bigler E.D. Neuropsychology and clinical neuroscience of persistent post-concussive syndrome. Journal of the International Neuropsychological Society. 2008. 14. P. 1–22.
https://doi.org/10.1017/S135561770808017X

10 Baevsky R.M., Kukushkin Y.A., Marasanov A.V., Romanov E.A. Methodology to evaluate the functional state of the human body. Moscow: Institute of Aviation and Space Medicine, 1995. P. 1–6 [in Russian].

11 Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996. 93 (5). P. 1043–1065.
https://doi.org/10.1161/01.CIR.93.5.1043

12 Korkushko O.V., Pisaruk A.V. The analysis of heart rate variability in clinical practice. Age-related aspects. Kiev: “Alcon”, 2002. p. 192 [in Russian].

13 Nuwer M.R., Comi G., Emerson R., Fuglsang-Frederiksen A, Gu?rit J.M, Hinrichs H., Ikeda A., Luccas F.J, Rappelsburger P. IFCN standards for digital recording of clinical EEG. International Federation of Clinical Neurophysiology. Electroencephalography and clinical Neurophysiology.1998. 106 (3). P. 259–261.
https://doi.org/10.1016/S0013-4694(97)00106-5

14 Lewicki P., Hill T.H. Statistics Methods and Applications. A Comprehensive Reference for Science, Industry, and Data Mining. Tulsa: StatSoft, 2006. p. 832.

15 Shanin Y.N. Medical rehabilitation of the wounded and sick. St. Petersburg: Special literature, 1997. p. 960 [in Russian].

16 Jokic-Begic N., Begic D. Quantitative electroencephalogram (qEEG) in combat veterans with post-traumatic stress disorder (PTSD). Nord J. Psychiatry. 2003. 57 (5). P. 351–355.
https://doi.org/10.1080/08039480310002688

17 Minassian A., Maihofer A.X., Baker D.G., Nievergelt C.M., Geyer M.A., Risbrough V.B. Association of Predeployment Heart Rate Variability With Risk of Postdeployment Posttraumatic Stress Disorder in Active-Duty Marines. JAMA Psychiatry. 2015. 10 (2). P. 979–986.
https://doi.org/10.1001/jamapsychiatry.2015.0922

18 Hayutin V.M., Lukoshkova E.V. Spectral analysis of heart rate fluctuations: the physiological basis and complicating its effects. Russian physiological journal. 1999. 85 (7). P. 893–908 [in Russian].

19 Tan G., Dao T.K., Farmer L., Sutherland R.J., Gevirtz R. Heart rate variability (HRV) and posttraumatic stress disorder (PTSD): a pilot study. Appl. Psychophysiol. Biofeedback. 2011. 36 (1). P. 27–35.
https://doi.org/10.1007/s10484-010-9141-y

Received 12.06.2017

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Issue 3 (189), article 4

DOI:https://doi.org/10.15407/kvt189.03.061

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Vovk М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: dep140@irtc.org.ua
Kutsyak A.А., PhD (Engineering), Researcher, Bioelectrical Control & Medical Cybernetics Department
e-mail: spirotech85@ukr.net
Lauta A.D., PhD (Medicine), Senior Researcher,
Bioelectrical Control & Medical Cybernetics Department
Ovcharenko М.А., Junior Researcher,
Bioelectrical Control & Medical Cybernetics Department
e-mail: ovcharenko-marina@i.ua
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

INFORMATION SUPPORT OF RESEARCHES ON THE DYNAMICS OF MOVEMENT RESTORATION AFTER THE STROKE

Introduction. The results of clinical testing of the innovative technology TRENAR® confirmed its effectiveness in motor and speech recovery after a stroke. The main advantage of the technology that enables a more efficient motor and speech recovery is advanced training programs, based on different methods. This allows one to select individual approach to the rehabilitation process. In order to determine a personalized recovery plan it is necessary to develop criteria to quantify motor recovery dynamics.
The purpose of the research is to determine criteria for separately quantifying recovery dynamics in proximal and distal parts of the upper and lower extremities, as well as to perform an integral quantitative assessment of the severity of motor function disorders after a stroke.
Results. The method for quantitative estimation of the effectiveness of motor function rehabilitation after a stroke was developed.
One special feature of the technique is separate quantitative assessment of the motor function deficit dynamics of the affected lower and upper extremities, their proximal and distal sections, including fine motor skills, according to basic and additional criteria. The technique allows us to study the contribution of these indicators to the integral quantitative evaluation of the effectiveness of motor recovery during rehabilitation process. The technique has successfully passed pilot clinical trials during the studies of motor function recovery dynamics after a stroke when innovative technology TRENAR® for motor training / recovery was used. It is intended for informational support of medical decision-making when devising an individual plan for the rehabilitation of motor and speech functions after a stroke.
Conclusion. The method for quantitative assessment of motor function recovery dynamics forms the basis for assessing the effectiveness of rehabilitation processes in patients after a stroke and for developing individual plans for rehabilitation. It serves as the digital informational support for physicians and will be essential for developing mobile applications for smartphones and tablets that can be used during the rehabilitation process.
Keywords: quantitative assessment, criteria, rehabilitation, effectiveness, stroke, motor functions, speech, disorders.

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REFERENCES

  1. Kolenko Ph.H., Stetsenko A.V., Stetsenko N.N. Optimization of the rehab process during cerebral stroke. Herald Sumy State University. Series Medicine. 2007. № 2. P. 61–66
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  8. Dolgova N.Yu., Vorontsov S.A., Kirichenko O.V., Makarova I.Yu. Rehabilitation treatment of neurological patients in a sanatorium. The Kremlin medicine. Clinical Herald. 2013, №4. P. 53–56 (in Russian).
  9. Belova A.N., Schepetova O.N. Scales, tests and questionnaires in medical rehabilitation M.: Antidor, 2002. 440 p. (in Russian).
  10. Kadykov A.S., Chernikova L.A., Shakhparonova N.V. Rehabilitation of Neurological Patients. М.: MEDpress-inform, 2008. 560 p. (in Russian).
  11. Smychek V.B., Ponomareva E.N. Craniocerebral injury (clinic, treatment, examination, rehabilitation). Mn.: Research Institute of ME and R, 2010. 430 p. (in Russian).
  12. Samsonova V., Barnikova I.E., Krestinina A.A. Non-invasive methods for evaluating the composition of muscle fibers. News of the Chernigiv National Pedagogical University. Ser.: Pedagogical science. Physical education and sport. Chernigiv, 2014. Vol. 118 (1). P. 323–326 (in Russian).
  13. Vovk M.I., Peleshok S.R., Galian E.B., Ovcharenko M.A. Method for assessing motor and sensory speech disorders. Collection of articles of the scientific and information center “Znanie” on the basis of the XI international correspondence scientific-practical conference: “The development of science in the XXI century” 3 part, Kharkov: a collection with articles (standard level, academic level).: Scientific and Information Center “Knowledge”, 2016. P. 70–76.

Received 9.06.2017

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Issue 3 (189), article 3

DOI:https://doi.org/10.15407/kvt189.03.044

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Balovsyak S.V., PhD (Phys-Math), Docent
Associate Professor (Docent) of the Department of Computer Systems and Networks
e-mail: s.balovsyak@chnu.edu.ua
Odaiska Kh.S., Postgraduate Student of the Department of Computer Systems and Networks
e-mail: k.odaiska@chnu.edu.ua
Yuriy Fedkovych Chernivtsi National University,
Kotsyubynsky St., 2, 58032, Chernivtsi, Ukraine

AUTOMATIC DETERMINATION OF LEVEL OF GAUSSIAN NOISE IN DIGITAL IM-AGES BY METHOD OF THE SELECTED REGIONS

Introduction. The noise level is an important parameter for common application tasks of digital image processing: noise removal, segmentation, recognition and other. At the experimental image processing the noise level in most cases is unknown, so development of a method for automatic and accurate determination of noise level in images is an actual and important for practice task [1–7]. Additive White Gaussian Noise (AWGN) belongs to the widespread noise model, since many noises in the real images are described rather accurately by the AWGN model [1]. For this reason this article will consider the methods of noise level determination in the images within the AWGN model, and this noise will further be called Gaussian noise for the sake of simplicity. Level of Gaussian noise is expressed by standard deviation of noise.
The purpose of the article is to develop an automatic method of Gaussian noise level determination in digital images, which uses the selection of image region based on its low-frequency filtering and performs calculation of noise level by analyzing of histograms of the selected region. The article is aimed at software implementation of the elaborated method in the MATLAB system and estimation of its accuracy by processing the collection of test images.
Methods. The method of the selected regions for calculation of Gaussian noise level, which involves the selection of image region and analysis of its histogram is used. Convolution operation to filter digital images is applied. For estimation of accuracy of noise level determination the root mean square error (RMSE) between the experimental and theoretical noise levels for the test images is used.
Results. The method of automatic determination of level of Gaussian noise in digital images is developed. A method consists in the selection of the image ROI (Region Of Interest) [1, 3], where the noise is mainly present, in the calculation of histogram of the selected region and standard deviation of histogram and in the calculation of experimental level of noise based on value . If the variation does not exceed the established limit, the process of the ROI clarification is completed. The proposed method is software implemented in MATLAB system [3].
Conclusion. The mathematical model of image filtering with Gaussian noise is created. The created model allows selecting the ROI regions with the prevailed Gaussian noise [8, 9]. The modification of the algorithm for determining the level of Gaussian noise in images is implemented [10–12], which specifies the minimum permissive value of the ROI area, leading to reducing the root mean square error of calculation of noise level on 0.1%.
The accuracy of the proposed method is studied by processing the set of 100 test images [11, 12], and the root mean square error of calculation equals to 0.257%. The resulting error of calculation is less, than the one obtained for the most accurate modern methods of determination of noise level [4, 5]. With the use of more precise method of the ROI noise level determination, different from histogram analysis, the accuracy of the proposed method can be improve.
Keywords: digital image processing, noise level determination, standard deviation of Gaussian noise, histogram of image.

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REFERENCES

1 Gonzalez R., Woods R. Digital image processing. M.: Technosphere, 2005. 1072 p. (in Russian).

2 Bovik A.L. The Essential Guide to Image Processing. Elsevier Inc., 2009. 853 p.

3 Gonzalez R., Woods R., Eddins S. Digital image processing using MATLAB. M.: Technosphere, 2006. 616 p. (in Russian).

4 Liu X., Tanaka M., Okutomi M. Single-Image Noise Level Estimation for Blind Denoising. IEEE Transactions on Image Processing. 2013. Vol. 22, No. 12. P. 5226–5237.
https://doi.org/10.1109/TIP.2013.2283400

5 Pyatykh S., Hesser J., Zheng L. Image noise level estimation by principal component analysis. IEEE Transaction on Image Processing. 2013. Vol. 22, No. 2. P.687–699.
https://doi.org/10.1109/TIP.2012.2221728

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7 Zoran D., Weiss Y. Scale invariance and noise in natural images. Proc. IEEE 12th Int. Conf. Comput. Vis., Sep./ Oct. 2009. P. 2209–2216.
https://doi.org/10.1109/ICCV.2009.5459476

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10 Image Processing Place. Image Databases. URL: http://www.imageprocessingplace.com/ root_files_V3/image_databases.htm.

11 Fowlkes C., Martin D., Malik J. Local Figure/Ground Cues are Valid for Natural Images. Journal of Vision. 2007. Vol. 7 (8), No. 2. P. 1–9.

12 The Berkeley Segmentation Dataset and Benchmark. BSDS300. URL: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds.

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Issue 3 (189), article 2

DOI:https://doi.org/10.15407/kvt189.03.029

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Zhiteckii L.S., PhD (Engineering), Acting Head of the Department of Intelligent Automatic Systems
e-mail: leonid_zhiteckii@i.ua
Solovchuk K.Yu., Postgraduate Student
e-mail: solovchuk_ok@mail.ru
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

DISCRETE-TIME STEADY-STATE CONTROL OF INTERCONNECTED SYSTEMS BASED ON PSEUDOINVERSION CONCEPT

Introduction. The problem of controlling interconnected systems subjected to arbitrary unmeasurable disturbances remains actual up to now. It is important problem from both theoretical and practical points of view. During the last decades, the internal model control principle becomes popular among other methods dealing with an improvement of the control system. A perspective modification of the internal model control principle is the so-called model inverse approach. Unfortunately, the inverse model approach is quite unacceptable if the systems to be controlled are square but singular or if they are nonsquare. It turned out that the so-called pseudoinverse (generalized inverse) model approach can be exploited to cope with the noninevitability of singular square and also nonsquare system.
The purpose of the paper is to generalize the results obtained by the authors in their last works which are related to the asymptotic properties of the pseudoinverse model-based method for designing an efficient steady-state control of interconnected systems with uncertainties and arbitrary bounded disturbances and also to present some new results.
Results. In this paper, the main effort is focused on analyzing the asymptotic properties of the closed-loop systems containing the pseudoinverse model-based controllers. In the framework of the pseudoinversion concept, new theoretical results related to the asymptotic behavior of these systems are obtained. Namely, in the case of nonsingular gain matrices with known elements, the upper bounds on the ultimate norms of output and control input vectors are found. Next, in the case of nonsquare gain matrices whose elements are also known, the asymptotic behavior of the feedback control systems designed on the basis of pseudoinverse approach are studied. Further, the sufficient conditions guaranteeing the boundedness of the output and control input signals for the linear and certain class of nonlinear interconnected systems in the presence of uncertainties are derived.
Conclusion. It has been established that the pseudoinverse model-based concept can be used as a unified concept to deal with the steady-state regulation of the linear interconnected discrete-time systems and of some classes of nonlinear interconnected systems with possible uncertainties in the presence of arbitrary unmeasured but bounded disturbances.
Keywords: discrete time, feedback, pseudoinversion, interconnected systems, optimality, stability, uncertainty.

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REFERENCES

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https://doi.org/10.1109/TAC.1972.1100084

2 Liu C., Peng H. Inverse-dynamics based state and disturbance observers for linear time-invariant systems. ASME J. Dyn Syst., Meas. and Control, 2002, vol. 124, no. 5, pp. 376–381.

3 Lyubchyk L. M. Disturbance rejection in linear discrete Multivariable systems: inverse model approach. Prep. 18th IFAC World Congress, Milano, Italy, 2011, pp. 7921–7926.
https://doi.org/10.3182/20110828-6-IT-1002.02121

4 Skogestad S., Postlethwaite I. Multivariable Feedback Control. UK, Chichester: Wiley, 1996.

5 Freudenberg J. and Middleton R. Properties of single input, two output feedback systems. Int. J. Control, 1999, vol. 72, no. 16, pp. 1446–1465.
https://doi.org/10.1080/002071799220100

6 Francis B., Wonham W. The internal model principle of control theory. Automatica, 1976, vol. 12, no. 5, pp. 457–465.
https://doi.org/10.1016/0005-1098(76)90006-6

7 Brockett R. W. The invertibility of dynamic systems with application to control. Ph. D. Dissertation, Case Inst. of Technology, Cleveland, Ohio, 1963.

8 Sain M. K., Massey J. L. Invertibility of linear time-invariant dynamical systems. IEEE Trans. Autom. Contr.,1969, vol. AC-14, no. 2, pp. 141–149, Apr. 1969.

9 Silverman L. M. Inversion of multivariable linear systems. IEEE Trans. Autom. Contr., 1969, vol. AC-14, no. 3, pp. 270–276.
https://doi.org/10.1109/TAC.1969.1099169

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https://doi.org/10.1080/00207177608932859

11 Seraji H. Minimal inversion, command tracking and disturbance decoupling in multivariable systems. Int. J. Control, 1089, vol. 49, no. 6, pp. 2093–2191.

12 Marro G., Prattichizzo D., Zattoni E. Convolution profiles for right-inversion of multivariable non-minimum phase discrete-time systems. Automatica, 2002, vol. 38, no. 10, pp. 1695–1703.
https://doi.org/10.1016/S0005-1098(02)00088-2

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https://doi.org/10.1016/S1474-6670(17)69194-8

16 Katkovnik V. Ya., Pervozvansky A. A. Methods for the search of extremum and the synthesis problems of multivariable control systems. Adaptivnye Avtomaticheskie Sistemy, Moscow: Sov. Radio, pp. 17–42, 1973 (in Russian).

17 Skogestad S., Morari M., Doyle J. Robust control of ill-conditioned plants: high purity distillation. IEEE Trans. Autom. Contr., 1988, vol. 33, no. 12, pp. 1092–1105.
https://doi.org/10.1109/9.14431

18 Skurikhin V. I., Zhiteckii L. S., Solovchuk K. Yu. Control of interconnected plants with singular and ill-conditioned transfer matrices based on pseudo-inverse operator method. Upravlyayushchye sistemy i mashiny, 2013, no. 3, pp. 14-20, 29 (in Russian).

19 Zhiteckii L. S., Azarskov V. N., Solovchuk K. Yu., Sushchenko O. A. Discrete-time robust steady-state control of nonlinear multivariable systems: a unified approach. Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 8140–8145.
https://doi.org/10.3182/20140824-6-ZA-1003.01985

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21 Albert A. Regression and the Moore-Penrose Pseudoinverse. New York: Academic Press, 1972.

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Received 17.02.2017

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Issue 3 (189), article 1

DOI:https://doi.org/10.15407/kvt189.03.005

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Orikhovska K.B., Postgraduate student,
Junior Researcher of the Department of Intelligent Automatic Systems
e-mail: kseniaor@gmail.com
Fainzilberg L.S., Dr (Engineering), Associate Professor (Docent), Chief Researcher of Data Processing Department
e-mail: fainzilberg@gmail.com

International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

COMPARATIVE ANALYSIS OF ESTIMATION METHODS OF THE PHYSIOLOGICAL SIGNALS VARIABILITY

Introduction. In the modern world, more attention is paid to the study of the behavior of complexly organized medical and biological systems. The fundamental concept of synergetics is the generalized entropy, which quantitatively characterizes the degree of the system chaoticness. Of special interest are studies of changes in the dynamic series chaotic parameters generated by various biological systems.
The purpose of the article is further development and experimental research of methods for analyzing the variability of physiological signals under external influences on the body.
Methods. Two alternative approaches of estimating the variability of dynamic series are investigated: based on the calculation of the sample variance relative changes and entropy estimates (in a sliding window with the specified parameters) in relation to the first window. The theoretical and experimental dependences between the Shannon entropy and the standard deviation for a normal distribution of a random variable that generates a dynamic series are studied. Comparison of these estimates with real and model data is carried out.
Results. To increase the sensitivity of entropy estimates to the variability of the dynamic series, it is proposed to move from a series of discrete entropy values at the -th point, calculated by the sliding window method, to its phase portrait on the plane , where is the estimate of the first derivative . For an integral assessment of the chaotic nature of physiological signals, it is suggested to estimate the area of the convex hull of the entropy phase portrait and the coordinates of the phase portrait gravity center , . Experimental studies have confirmed the diagnostic value of these parameters in the assessment of variability of the electrocardiograms and rhythmograms indices with external influences on the body (intravenous therapy, surgery and physical activity).
Conclusions. Deviations of the integral parameters of the entropy phase portrait under the effect of external influences on the organism were detected, which open new possibilities in the evaluation of the cardiac activity regulation in preventive and clinical medicine. These integral parameters require further study to confirm their statistical significance in representative samples of observations.

Keywords: variability of physiological signals, entropy estimates, diagnostic criteria.

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https://doi.org/10.3390/e16115698

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Received 5.06.2017