skip to main content
10.1145/3167486.3167540acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccwcsConference Proceedingsconference-collections
research-article

Ischemia Detection Based on ST Segment Level using Wavelet Transform

Published:14 November 2017Publication History

ABSTRACT

In this work, we present an algorithm for detection of myocardial Ischemia in electrocardiogram (ECG) based on the identification of the isoelectric line and ST segment deviation. During the preprocessing stage, Wavelet Packet Transform WPT is used to remove the baseline wander and power line interference (PLI) in the ECG signal. To locate the positions of the heartbeat waves (QRS complex, P wave, and T wave), the decomposition is used with Discrete Wavelet Transform (DWT) up to level 8. ST segment level was estimated based on the isoelectric level. The algorithm was evaluated against The Long-Term ST Database (LTSTDB).

References

  1. http://www.heart.org/HEARTORG/Conditions/HeartAttack/Treatmento%20aHeartAttack/Silent-Ischemia-and-Ischemic-Heart%20Disease_UCM_434092_Article.jsp#.WXoTQYTyjIWGoogle ScholarGoogle Scholar
  2. Shirley A. Jones. 2008. ECG Success Exercises in ECG Interpretation (F. A. Davis Company, 2008)Google ScholarGoogle Scholar
  3. S.Yu, P.Tsai. 2016. Myocardial Ischemic Beat and Episode Detection based on Morphology and Correcting Window Method. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (2016), 3465--3468.Google ScholarGoogle ScholarCross RefCross Ref
  4. M.K.Moridani, M.Pouladian. 2009. Detection ischemic episodes from electrocardiogram signal using wavelet transform. J. Biomedical Science and Engineering, (2009), 239--244.Google ScholarGoogle ScholarCross RefCross Ref
  5. M.Hadjem, F.Naït-Abdesselam, A.Khokhar. 2016. ST-segment and T-wave Anomalies Prediction in an ECG Data Using RUSBoost. IEEE 18th International Conference on e-Health Networking, Applications and Services, (2016), 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  6. S.T.Prasad, S.Varadarajan, Classification of ST-Segments in ECG using ICA and Triangle Method. 2016. International Conference on Communication and Signal Processing, (2016), 2119--2123.Google ScholarGoogle Scholar
  7. K.Vimala, Dr.V.Kalaivani. 2013. Classification of cardiac vascular disease from ECG signals for enhancing modern health care scenario. Health Informatics- An International Journal (HIIJ), (2013) Vol.2, No. 4, 63--72.Google ScholarGoogle Scholar
  8. W.Cong, W.Xiaomei. 2015. Significant Difference Analysis of Myocardial Ischemia Indicators based on Synthesized Algorithm. IEEE International Conference on Digital Signal Processing. pp. 124 -128, 2015.Google ScholarGoogle Scholar
  9. N. Vasudha, N. Sundararajan. 2014. Early Detection of Ischemia from ECG by Wavelet Analysis. International Conference on Computing and Communication Technologies. (2014), 1--4.Google ScholarGoogle ScholarCross RefCross Ref
  10. A. Amal, G. Reshmi. 2013. Cardiac Ischemia Diagnosis Using Stress ECG Analysis. Third International Conference on Advances in Computing and Communications, (2013), 204--207. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C.H.Fan, Y.Hsu; S.Yu, J.W.Lin. 2013. Detection of Myocardial Ischemia Episode Using Morphological Features. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013), 7334--7337.Google ScholarGoogle Scholar
  12. R.Valupadasu, B.Chunduri, V.Chanagoni. 2012. Identification of Cardiac Ischemia Using Bispectral Analysis of ECG. IEEE-EMBS Conference on Biomedical Engineering and Sciences, (2012), 999 - 1003.Google ScholarGoogle Scholar
  13. P.X.Quesnel, A.D.C.Chan, H.Yang. 2013. Real-Time Biosignal Quality Analysis of Ambulatory ECG for Detection of Myocardial Ischemia. IEEE International Symposium on Medical Measurements and Applications (MeMeA). pp. 1 - 5, 2013.Google ScholarGoogle Scholar
  14. L.Dranca, A.Goni, A.Illarramendi. 2009. Real-time Detection of Transi3ent Cardiac Ischemic Episodes from ECG Signals. Physiol. Meas. 30, (2009), 983--992.Google ScholarGoogle Scholar
  15. F.Jager et al. 2003. Long-term ST Database: A Reference for The Development and Evaluation of Automated Ischemia Detectors and for the Study of the Dynamics of Myocardial Ischemia. Medical & Biological Engineering & Computing, 41(2), (2003), 172--183.Google ScholarGoogle ScholarCross RefCross Ref
  16. A.Goldberger et al. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, Circulation. 101(23), (June 13 2000), e215--e220.Google ScholarGoogle ScholarCross RefCross Ref
  17. S. Mallat. 1989. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. on Patt. Anal. and Mach. Intell, (1989), 674--693. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Bailey et al. Recommendations for the Standardization and Specifications in Automated Electrocardiography: Bandwidth and Signal Processing. Circulation, vol. 81, (1990), 730--739.Google ScholarGoogle Scholar
  19. Z. Zidelmal, A. Amirou, M. Adnaneb, A. Belouchranib. QRS Detection Based on Wavelet Coefficients. Elsevier, Computer Methods and Programs in Biomedicine, 107, (2012), 490--496. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J.Pan, J. Tompkins. A Real-Time QRS Detection Algorithm. IEEE Trans. Biomed. Eng. BME, 32 (3), (1985), 230--236.Google ScholarGoogle ScholarCross RefCross Ref
  21. M. Elhanine, E. Abdelmounim, R. Haddadi, A. Belaguid. An Alternative Application of The Wavelet Packet Transform to Remove Noises in The ECG Signals. International Review on Computers and Software, Vol. 10, No. 8, (2015), 838--844.Google ScholarGoogle Scholar
  22. R. Haddadi, E. Abdelmounim, M. Elhanine, A. Belaguid. Discrete Wavelet Transform Based Algorithm for Recognition of QRS Complexes. World of Computer Science and Information Technology Journal (WCSIT), Vol. 4, No. 9, (2014), 127--132.Google ScholarGoogle Scholar
  23. N.V. Thakor, J.G. Webstor, W.J. Thompkins. Estimation of the QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng, 31 (11), (1984), 702--706.Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    ICCWCS'17: Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems
    November 2017
    512 pages
    ISBN:9781450353069
    DOI:10.1145/3167486

    Copyright © 2017 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 14 November 2017

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited
  • Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader