DOI:https://doi.org/10.15407/kvt183.01.005
Fainzilberg Leonid S., Dr of Engineering, Chief Researcher of Data Processing Department of International Research and Training Center for Information Technologies and Systems National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine, av. Acad. Glushkova, 40, Kiev, 03680.
e-mail: fainzilberg@voliacable.com
Orikhovska Kseniya B.,Postgraduate (PG) of International Research and Training Center for Information Technologies and Systems National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine, av. Acad. Glushkova, 40, Kiev, 03680,
e-mail: kseniaor@gmail.com
Vakhovskyi Ivan V., Student of National Technical University of Ukraine “Kiev Polytechnical Institute”, av. Pobedy, 37, Kiev, 03056,
e-mail: evanvaha@gmail.com
ASSESSMENT OF CHAOTIC FRAGMENTS’ SHAPE OF THE SINGLE-CHANNEL ELECTROCARDIOGRAM. Kibernetika i vyčislitel’naâ tehnika, 2016, issue 183, pp.4-24.
Introduction. Building an effective information technology (IT), which provides chaotic assessment of the electrocardiogram (ECG) fragments’ shape, has both cognitive and practical importance. Therefore, the problem of developing methods and computer tools that provide assessment not only by the rhythm of the heart, but also on other parameters of ECG that have diagnostic value is relevant.
The purpose of the article is to propose instrumental system for the study of single-channel ECG elements shape chaoticity, based on the various entropy assessments and make a comparative analysis of these estimates in the model and the real data.
Methods. The proposed instrumental system based on the national portable electrocardiograph FAZEGRAF® with the original finger electrodes sensor, which can record the ECG from the first standard lead. In addition to determining the chaoticity of parameters, that characterize the shape of the main elements of the ECG, also estimating the diagnostic features chaoticity. Introduced a number of improvements in the considered methods which determine the signal chaoticity degree. In particular, an original evaluation algorithm for permutation entropy evaluating that can automatically identify 5 classes of patterns is proposed.
Results. Processing of model and real data showed that the computing algorithms implemented in IT allow to adequately assessing the degree of signals chaoticity. Based on the parameters chaotic assessment, that carry information about the ECG elements shape, diagnostically important subtle signal differences in healthy and sick patients, as well as significant differences in parameters of the ECG elements shape chaoticity in people with varying degrees of organism fitness were found.
Conclusions. Instrumental system provides the convenience of experimental studies with searching for new biomarkers of cardiac abnormalities and evaluation of organism adaptation capabilities.
Keywords: heart rate, the entropy of the process, synergy, shape of ECG fragments.
References
1 Tuzov V.V. Methods of synergetics. Bibliosphere, 2009, No. 4, pp. 8–14. (in Russian).
2 Bian C., Qin C., Ma Q.D. et. al. Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 2012, No. 85.
3 Parlitz U., Beg S.L., Schirdewan S. et al. Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics. Computers in Biology and Medicine, 2012, No. 42, pp. 319–327. https://doi.org/10.1016/j.compbiomed.2011.03.017
4 Frank B., Pompe B., Schneider U. et al. Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses. Medical & Biological Engineering & Computing, 2006, No. 44, pp. 179–187. https://doi.org/10.1007/s11517-005-0015-z
5 Durnova N.Y., Dovgalevskij Y.P., Burlaka A.N. et al.The study of relationships between parameters of variation pulsometry, entropy of heart rate, time and spectral analysis of heart rate variability in normal and ischemic heart disease. Saratov Medical Scientific Research Journal, 2011, Vol. 7, No. 3, pp. 608–611. (in Russian).
6 Wentsel E.S. Theory of probability. Moscow: Science, 1969, 575 p. (in Russian).
7 Kuznecov A.A. Methods of analysis and processing of ECG signals: new approaches to information extraction: monograph. Vladimir: Vladimir State University Publishing, 2008, 140 p. (in Russian).
8 Sotnikov P.I. Isolation of the characteristic features of a EEG signal by the entropy analyzing. Science and Education, 2014, No. 11, pp. 555–570. (in Russian).
9 Nermiko A.P., Manilo L.A., Kalinichenko A.N. et.al. Comparative analysis of the different estimates usage of the EEG signal entropy for recognizing the anesthesia stages. Biotechnosphere, 2010, No. 3, pp. 3–10. (in Russian).
10 Takens F. Detecting strange attractors in turbulence. Dynamical systems and turbulence: lecture notes in mathematics, 1981, Vol. 898, pp. 366–381.
11 Pincus S.M. Approximate entropy as a measure of system complexity. Proc. of the National Academy of Sciences, 1991, Vol. 88, pp. 2297–2301. https://doi.org/10.1073/pnas.88.6.2297
12 Danilchuk A.B. Using entropy parameters for simulating the dynamics of complex social and economic systems. Economics, 2014, No. 3, pp. 19–24. (in Russian).
13 Jushkovskaja O.G. A new approach to assessing the effectiveness of sanatorium rehabilitation of patients with coronary heart disease. Physical education in the prevention, treatment and rehabilitation, 2004, No. 1, pp. 22–26. (in Russian).
14 Costa M., Ary L., Goldberger A.L. Multiscale entropy analysis of biological signals. Physical Review E, 2005, No. 71.
15 Pincus S.M., Goldberger A.L. Physiological time-series analysis: what does regularity quantify? The American journal of physiology, 1994, Vol. 266, pp. 1643–1656. https://doi.org/10.1152/ajpheart.1994.266.4.H1643
16 Joshua S. Richman J., Moorman R. Physiological time-series analysis using approximate entropy and sample entropy. The American journal of physiology, 2000, Vol. 278, No. 6, pp. 2039–2049.
17 Bandt C., Pompe B. Permutation entropy — a natural complexity measure for time series. Chinese Physics B, 2001, Vol. 18, No. 7, pp. 2690–2696.
18 Antipov I.E., Zakharov A.V., Poverennova O.I. The possibilities of different methods of automatic recognition of sleep stages. Saratov Medical Scientific Research Journal, 2012, Vol. 8, No. 2, pp. 374–379. (in Russian).
19 Mun F. Chaotic oscillations: Introductory course for scientists and engineers: Trans. from English. Moscow: Mir, 1990, 312 p. (in Russian).
20 Mayorov O.Y., Feshchenko V.N. Improving the reliability of studies of deterministic chaos in the bioelectric activity (EEG, ECG and heart rate variability) methods of nonlinear analysis. Clinical Informatics and Telemedicine, 2009, Vol. 6, No 6, pp. 10–17. (in Russian).
21 Kiseleva O.G., Nastenko Ie.A., Gerasimchuk M.V. Method of estimation disadaptation states of the human body. East European Journal of advanced technologies, 2011, Vol. 3, No. 2, pp. 57–64. (in Russian).
22 Zinenko A.V. R/S analysis of the stock market. Business Informatics, 2012, No 3, pp. 24–30. (in Russian).
23 Apanasenko G.L., Chistyakova Yu. S. Athlete’s Health Criteria evaluation and prediction. Theory and Practice of Physical Culture, 2006, No 1, pp.19–22. (in Russian).
24 Fainzilberg L.S. Computer diagnostics by phase portrait of electrocardiogram. Kiev: Osvita Ukrainy, 2013, 191 p. (in Russian).
25 Zaguskin S.L., Borisov V.A. A method for diagnosing the functional state of human and animal. RU Patent 2254051, 2006, bul. 13. (in Russian).
Received 08.12.2015