INFORMATIVE PARAMETERS OF DYNAMIC NONSTATIONARY OF CARDIOSIGNALS

Authors

  • P. F. Shchapov National Technical University “Kharkov Polytechnic Institute”, Kharkov, Ukraine., Ukraine
  • S. N. Koval State Enterprise “National Institute of Therapy named after L.T. Maloy, National Academy of Sciences of Ukraine”, Kharkov, Ukraine., Ukraine
  • E. I. Korol National Technical University “Kharkov Polytechnic Institute”, Kharkov, Ukraine., Ukraine
  • R. S. Tomashevskyi National Technical University “Kharkov Polytechnic Institute”, Kharkov, Ukraine., Ukraine
  • T. I. Mahdalyts Kharkov Medical Academy of Postgraduate Education, Department “Therapy, Nephrology and Family Medicine”, Kharkov, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2018-1-3

Keywords:

quantization of cardiac signal speed, spectral nonstationarity, wavelet transform, cardiac signal transformation, correlation of wavelet spectra.

Abstract

Contex. Modern electrocardiography, in spite of qualitative improvement in hardware and data processing capabilities, for today has
practically exhausted a resource of reception of the additional diagnostic information. In the article an attempt is made to create a new method
for processing electrocardiograms based on the use of the ECG signal model, which takes into account the piezoelectric effect in some
biological tissues and cell connections (blood, vessel walls).
Objective. Probabilistic justification of the possibility of forming fundamentally new informative diagnostic features, which uses the
time-frequency correlation between two wavelet spectra of the ECG signal and its linear transformation. Method. As such a model is used the additive model of the potential of the cardiac muscle (induced electric field) and the piezoelectric
potential of the blood-vessel system caused by myocardial contraction. To isolate the influence of the induced potential is proposed a method
of linear transformation ECG signal. This method has a high sensitivity to local spectral nonstationarity. Wavelet transform is used to
implement this method. The coefficient of normalized inter-spectral correlation (CNIC) is proposed as a quantitative indicator of the spectral
nonstationarity of the ECG signal. The developed mathematical apparatus in the work is used for the analysis of two electrocardiographic
signals: conditional norm and with the consequence of myocardial infarction.
Results. As a result of the calculated CNIC, the possibility of a quantitative difference of these states with a sufficiently high statistical
reliability is shown. The basic result of the work is a probabilistic justification for the possibility of forming fundamentally new informative
diagnostic features using the time-frequency correlation between two wavelet spectra of an ECG signal and its linear transformation. High
sensitivity and information significance of correlation diagnostic features are confirmed by examples of discrimination of parametrically
inhomogeneous ECG signals.
Conclusions. Main results of the study: the spectral non-stationarity of the cardiac signal has been confirmed theoretically and
experimentally; The functional interrelation of the spectral nonstationarity of the ECG signal with the effects of quantization of the rate of its change is obtained; A method for the parametric determination of the coefficient of inter-spectral correlation was developed, which makes it possible to quantitatively describe the dynamics of the local spectral changes in the cardiac signal for the tasks of automatic express control and diagnostics of cardiac states and carried out its approbation.

References

Karimipour Atiyeh, Mohammad Reza Homaeinezhad Real-time

electrocardiogram P-QRS-T detection – delineation algorithm

based on quality-supported analysis of characteristic templates,

Computers in Biology and Medicine, 2014, P. 153–165.

DOI: 10.1016/j.compbiomed.2014.07.002.

Rudenko M. Y., Krstačić G. New philosophy of validation and

verification for cardiology: classical proof theory imported from

natural sciences, Cardiometry, 2014, No. 4, pp. 16–30. DOI:

12710/cardiometry.2014.4.1630

Sur M. S., Dandapat S. Wavelet-based Electrocardiogram signal

compression methods and their performances: A prospective

review-Biomedical Signal Processing and Control 14, 2014,

pp. 73–107.

Chouakri S. A., Bereksi-Reguig F., Ahmaпdi S., Fokapu O. Wavelet

Denoising of the Electrocardiogram Signal Based on the Corrupted

Noise Estimation, IEEE, 2005, рp. 1021–1024. DOI: 10.1109/

CIC.2005.1588284

Sasikala P., Banu W. Extraction of P wave and T wave in

Electrocardiogram using Wavelet Transform, International

Journal of Computer Science and Information Technologies,

Vol. 2 (1), 2011, рр. 489–493.

Avt. Kol.: D. V. Klark ml., M. R. Niuman, V. Kh. Olson y dr., Red.

Dzhon H. Vebster Medytsynskye prybory: Razrabotka y

prymenenye. Kiev, Medtorh, 2004, 620 p.

Halperin C., Mutchnik S., Agronin A., Molotskii M., Urenski P.,

Salai M., Rosenman G. Piezoelectric Effect in Human Bones

Studied in Nanometer Scale. Department of Orthopedic Surgery,

Beilinson Campus, Rabin Medical Center, Petah-Tiqwa, 49100,

Israel, and Department of Electrical Engineering-Physical

Electronics, School of Engineering, Tel Aviv University, Ramat-

Aviv. Israel, 2004, pp. 1253–1256. DOI: 10.1021/nl049453i

Catalin Harnagea, Martin Valli res, Christian P. Pfeffer, Dong

Wu, Bjorn R. Olsen, Alain Pignolet, Fran ois L gar , Alexei

Gruverman Two-Dimensional Nanoscale Structural and Functional

Imaging in Individual Collagen Type I Fibrils, Biophys J., 2010,

Jun 16, pp. 3070–3077. DOI: 10.1016/j.bpj.2010.02.047.

Boiko V. V., Bandurian B. B., Bulat E. A. y dr.; pod obshch.

red. V. V. Boiko, E. Y. Sokola, P. N. Zamiatyna Pezobyosyntez:

predposylky, hypotezy, fakty: monohr. V 4-kh t. Vol. 4. Kharkov,

Yzd-vo «Pidruchnyk NTU “KhPI”», 2017, 656 p. Na rus. Yaz.

Smith D. R., Holland A. D., Hutchinson I. B. Random telegraph

signals in charge coupled devices, Nuclear Instruments and Methods

in Physics Research, 2004, 15 p. DOI. org/10.1016/

j.nima.2004.03.210

Mohammad Azim Karami, Cristiano Niclass, Edoardo Charbon

Random Telegraph Signal in Single-Photon Avalanche Diodes,

International Image Sensor Workshop. Bergen, Norway, IISW,

, pp. 1–4.

Jun Li. A Wavelet Approach to Edge Detection: a thesis to The

Department of Mathematics and Statistics in partial fulfillment

of the requirements for the degree of Master of Science in the

subject of Mathematics. Huntsville, Texas, 2003, 80 р.

Hurd H. L., Miamee A. Periodically Correlated Random Sequences.

Spectral Theory and Practice. New Jersey, Wiley-Interscience,

, 353 p.

Hinich M. J. A statistical theory of signal coherence, IEEE J.

Oceanic Engineering. Apr. 2000, Vol. 25, No. 2, P. 256–261.

DOI: 10.1109/48.838988.

Fraley Chris, Raftery Adrian E. Model-Based Clustering,

Discriminant Analysis, and Density Estimation, Journal of the

American Statistical Association, 2002, No. 458, Vol. 97,

pp. 611–631.

Georg H., Langley P. Estimating Continuous Distributions in

Bayesian Classifiers, Proceedings of the Eleventh Conference on

Uncertainty in Artificial Intelligence. San Mateo: Morgan

Kaufmann, 1995, рp. 338–345.

How to Cite

Shchapov, P. F., Koval, S. N., Korol, E. I., Tomashevskyi, R. S., & Mahdalyts, T. I. (2018). INFORMATIVE PARAMETERS OF DYNAMIC NONSTATIONARY OF CARDIOSIGNALS. Radio Electronics, Computer Science, Control, (1), 22–29. https://doi.org/10.15588/1607-3274-2018-1-3

Issue

Section

Radio electronics and telecommunications