INFORMATIVE PARAMETERS OF DYNAMIC NONSTATIONARY OF CARDIOSIGNALS
DOI:
https://doi.org/10.15588/1607-3274-2018-1-3Keywords:
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 haspractically 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.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2018 P. F. Shchapov, S. N. Koval, E. I. Korol, R. S. Tomashevskyi, T. I. Mahdalyts
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.