Development of alternative diagnostic feature system in the cardiology decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2016.71949

Keywords:

alternative feature space, electrocardiogram, premature ventricular contractions, hodograph

Abstract

The trend towards an increase in the production of Ukrainian digital electrocardiographic telemetry systems such as transtelephonic digital 12-channel electrocardiograph complex “Telecard” identified the need to create intelligent automated cardiac decision support systems. The basis of these systems is the morphologic analysis of electrocardiograms, which represent biomedical signals with locally concentrated features.

The system of alternative diagnostic features based on the method proposed by the authors of the morphological analysis of biomedical signals with locally concentrated features to provide additional graphical information in the diagnosis of one of the most common cardiac arrhythmias - ventricular arrhythmia is developed. Representation of the electrocardiogram in two-dimensional space of alternative features, as well as hodograph is proposed. Differences between the ECG-hodographs for normal ECG and ECG with different arrhythmias of right and left ventricles, as well as multifocal ventricular arrhythmia are analyzed. It was found that a graphical representation of an electrocardiogram in the alternative feature space allows the physician to visually perform the classification of different types of ventricular arrhythmia, which in combination with the classical analysis of ECG on the time axis increases the reliability of diagnostics.

Author Biographies

Anatoly Povoroznyuk, National Technical University «Kharkiv Polytechnic Institute» Bagaliya str., 21, Kharkiv, Ukraine, 61002

Doctor of Technical Sciences, professor

Department of Computer Engineering and Programming

Anna Filatova, National Technical University «Kharkiv Polytechnic Institute» Bagaliya str., 21, Kharkiv, Ukraine, 61002

PhD, associate professor

Department of Computer Engineering and Programming

References

  1. Vladzymyrskyi, A. V. (2001). Telemedytsyna: monohrafyia. Donetsk, OOO «Tsyfrovaia typohrafyia», 437.
  2. Fainzylberh, L. S., Soroka, T. V. (2015). Development of telemedicine system for remote monitoring of heart activity based on fasegraphy method. Eastern-European Journal of Enterprise Technologies, 6 (9 (78)), 37–46. doi: 10.15587/1729-4061.2015.55004
  3. Mykoliuk, V. V., Lozovych, V. A. (2010). Pidsumky ekspluatatsii ustatkuvannia dystantsiinoi reiestratsii EKH «Kompleks medychnyi diahnostychnyi «Tredeks»» za 2009 rik v Mohyliv–Podilskomu raioni Vinnytskoi oblasti. Ukrainskyi zhurnal telemedytsyny ta medychnoi telematyky, 8 (2), 182–186.
  4. Vladzymyrskyi, A. V., Pavlovych, R. V., Mozghovoi, V. V. (2012). Ob'ektyvyzatsyia effektyvnosty telemedytsynskoi sety «Telekard». Ukrainian Journal of Telemedicine and Medical Telematics, 10 (2), 4–12.
  5. Hampton, J. R. The ECG Made Easy (2013). Churchill Livingstone, 208.
  6. Khoór, S., Kecskés, I., Kovács, I., and others (2008). Heart Rate Analysis and Telemedicine: New Concepts & Maths. Acta Polytechnica Hungarica, 5 (1), 136–145.
  7. Yokokawa, M., Kim, H. M., Good, E., Chugh, A., Pelosi, F., Alguire, C. et. al. (2012). Relation of symptoms and symptom duration to premature ventricular complex–induced cardiomyopathy. Heart Rhythm, 9 (1), 92–95. doi: 10.1016/j.hrthm.2011.08.015
  8. Saurav, A., Smer, A., Abuzaid, A., Bansal, O., Abuissa, H. (2015). Premature Ventricular Contraction-Induced Cardiomyopathy. Clinical Cardiology, 38 (4), 251–258. doi: 10.1002/clc.22371
  9. Fainzylberh, L. S. (2015). Obobschennyiy metod obrabotki tsiklicheskih signalov slozhnoy formyi v mnogomernom prostranstve parametrov. International Scientific and Technical Journal "Problems of control and informatics", 2, 58–71.
  10. Salih, S. K., Aljunid, S. A., Yahya, A., Ghailan K. (2012). A Novel Approach for Detecting QRS Complex of ECG signal. International Journal of Computer Science Issues, 9 (6 (3)), 205–215.
  11. Karimipour, A., Homaeinezhad, M. R. (2014). Real-time electrocardiogram P-QRS-T detection–delineation algorithm based on quality-supported analysis of characteristic templates. Computers in Biology and Medicine, 52, 153–165. doi: 10.1016/j.compbiomed.2014.07.002
  12. Kligfield, P., Gettes, L. S., Bailey, J. J. et. al. (2007). Recommendations for the standardization and interpretation of the electrocardiogram, part I: the electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Circulation, 115, 1306–1324.
  13. Abawajy, J. H., Kelarev, A. V., Chowdhury, M. (2013). Multistage approach for clustering and classification of ECG data. Computer Methods and Programs in Biomedicine, 112 (3), 720–730. doi: 10.1016/j.cmpb.2013.08.002
  14. Pasolli, E., Melgani, F. (2015). Genetic algorithm-based method for mitigating label noise issue in ECG signal classification. Biomedical Signal Processing and Control, 19, 130–136. doi: 10.1016/j.bspc.2014.10.013
  15. Serbest, K., Bozkurt, M. R., Eldoğan O. (2015). Classification of cardiac arrhythmias with artificial neural networks according to gender differences. Journal of Engineering Science and Technology, 10 (9), 1144–1149.
  16. Ceylan, R., Özbay, Y., Karlik, B. (2009). A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications, 36 (3), 6721–6726. doi: 10.1016/j.eswa.2008.08.028
  17. Doğan, B., Korürek, M. (2012). A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Applied Soft Computing, 12 (11), 3442–3451. doi: 10.1016/j.asoc.2012.07.007
  18. Lin, C.-H. (2008). Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Computers & Mathematics with Applications, 55 (4), 680–690. doi: 10.1016/j.camwa.2007.04.035
  19. Fazlul Haque, A. K. M., Hanif Ali, Md., Adnan Kiber, M., Tanvir Hasan Md. (2009). Detection of small variations of ECG features using Wavelet. ARPN Journal of Engineering and Applied Sciences, 4 (6), 27–30.
  20. Povoroznyuk, A. I., Filatova, A. E., Surtel, W., Burlibay, A., & Zhassandykyzy, M. (2015). Design of decision support system when undertaking medical-diagnostic action. Optical Fibers and Their Applications 2015, 98161O1–98161O17. doi: /10.1117/12.2229295
  21. Povoroznyuk, A. I., Filatova, A. E. (2014). Proektirovanie nelineynogo filtra v zadache strukturnoy identifikatsiya biomeditsinskih signalov s lokalno sosredotochennyimi priznakami. Systemni doslidzhennia ta informatsiini tekhnolohii, 1, 69–80.
  22. Burtsev, M., Povoroznuk, A., Povoroznuk, O., Filatova, A. (2013). Design of Computer–Based Intelligent Support Decision Systems for Medicine. XII international conferences "The experience of designing and application of CAD systems in microelectronics", 45–52.
  23. Povoroznyuk, A. (2013). Design of Non-Linear Filter in the Problem of Structural Identification of Biomedical Signals with Locally Concentrated Properties. Science Journal of Circuits, Systems and Signal Processing, 2 (3), 85. doi: 10.11648/j.cssp.20130203.12

Downloads

Published

2016-06-27

How to Cite

Povoroznyuk, A., & Filatova, A. (2016). Development of alternative diagnostic feature system in the cardiology decision support systems. Eastern-European Journal of Enterprise Technologies, 3(9(81), 39–44. https://doi.org/10.15587/1729-4061.2016.71949

Issue

Vol. 3 No. 9(81) (2016): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2016 Anatoly Povoroznyuk, Anna Filatova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.