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.
REFERENCES
1 Klimontovich Yu.L. Introduction to physics of open systems. Moscow: Janus–K; 2002. 284 p.
2 Martin-Sanchez F., Iakovidis I., Norager S., Maojo V., de Groen P., Van der Lei J., Jones T., Abraham-Fuchs K., Apweiler R., Babic A., Baud R., Breton V. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. Journal of Biomedical Informatics. 2004. Vol. 37. N 1. P. 30–42.
https://doi.org/10.1016/j.jbi.2003.09.003
3 Weippert M., Behrens M., Rieger A., Behrens K. Sample entropy and traditional measures of heart rate dynamics reveal different modes of cardiovascular control during low intensity exercise. Entropy. 2014. Vol. 16. P. 5698–5711.
https://doi.org/10.3390/e16115698
4 Durnova N.Yu., Dovgalevskiy Ya.P., Burlaka A.N., Kiselev A.R., Furman N.V. Interdependence of parameters of variational pulsometry, entropy of heart rate, temporal and spectral analyses of heart rate variability in normal state and in ischemic heart disease. Saratov journal of medical scientific research. 2011. Vol. 7. N 3. P. 607–611.
5 Ban A.S., Paramonova N.A., Zagorodnyy G.M., Ban D.S. Analysis of the relationship of heart rate variability indices. Voennaya Meditsina. 2010. N 4. P. 21–24.
6 Joshua S., Richman J., Moorman R. Physiological time-series analysis using approximate entropy and sample entropy. The American journal of physiology. 2000. Vol. 278. N 6. P. 2039–2049.
7 Peng C.K., Havlin S., Stanley H.E., Goldberger A.L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995. Vol. 5. P. 82–87.
https://doi.org/10.1063/1.166141
8 Iyengar N., Peng C.K., Morin R., Goldberger A.L., Lipsitz L.A. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 1996. Vol. 271. P. 1078–1084.
https://doi.org/10.1152/ajpregu.1996.271.4.R1078
9 Mayorov O.Yu., Fenchenko V.N. Calculation of the correlation dimestion and entropy of EEG signals in cluster computing systems. Clinical informatics and telemedicine. 2014. Vol. 10. N 11. P. 10–20.
10 Anishchenko V.S., Saparin P.I. Normalized entropy as a diagnostic criterion of human cardio-vascular system reaction on the external influence. Izvestia VUZ. Applied nonlinear dynamics. 1993. Vol. 1. N 3–4. P. 54–64.
11 Shapovalov V.I. About the fundamental laws of trend management. Control Science. 2005. Vol. 2. P. 2–11.
12 Yashin A.A. Living matter. Physics of the alive and evolutionary processes. Moscow: LKI, 2010. 264 p.
13 Zhukovska O.A., Glushauskene G.A., Fainzilberg L.S.Research of the modified estimation properties of random variable’s variance on sample of different observations. Naukovi Visti NTUU KPI. 2008. N 4. P. 139–145.
14 Fainzilberg L.S., Orikhovska K.B., Vakhovskyi I.V. Assessment of chaotic fragments’ shape of the single-channel electrocardiogram.Cybernetics and computer engineering. 2016. Vol. 183. P. 4–24.
15 Gorban I.I. Entropy of uncertainty. Mathematical Machines and Systems. 2013. N 2. P. 105–117.
16 Afanasyev V.V. Theory of Probability: a textbook for university students studying in the specialty “Mathematics”. M.: The Humanitarian publishing center VLADOS, 2007. 350 p.
https://doi.org/10.1137/S0040585X97982700
17 Kramarenko S.S. Method of use of the entropy-information analysis for quantitative attributes. Proceedings of the Samara Scientific Center of the RAS. 2005. Vol. 7. N 1. P. 242-247.
18 Fainzilberg L.S. Information technology for signal processing of complex shape. Theory and practice. Kiev: Naukova Dumka, 2008. 333 p.
19 Fainzilberg L.S. Fasegraphy basics. Kyiv: Osvita Ukrainy, 2017. 264 p.
20 Rosenbaum D.S., Jackson L.E., Smith J.M. Electrical alternans and vulnerability to ventricular arrhythmias. New England Journal of Medicine. 1994. Vol. 330. P. 235–241.
https://doi.org/10.1056/NEJM199401273300402
21 Fainzilberg L.S., Bekler T.Yu. T-Wave Alternats Modeling on artificial electrocardiogram with internal and external perturbation. Journal of Automation and Information Sciences. 2012. Vol. 44. N 7. P. 1–14.
https://doi.org/10.1615/JAutomatInfScien.v44.i7.10
22 Vlasova I.V. There are more and more side effects in drugs. Commercial biotechnology. 2007. Vol. 10. P. 14–19.
Received 5.06.2017