Skip to main content
SpringerLink
Log in

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) characterized by end-diastolic/systolic volumes (EDV/ESV) and heart rate. The pumping of blood from left ventricle (LV) reconciles in to R-R intervals depicted on ECG, which are used for heart rate estimation. The deviation from the nominal values of CO implies that, the subject is more prone to LVA. Further, the identification of subjects with LVA is accomplished by computing the features from the ECG signals. The proposed Feature Ranking Score (FRS) algorithm employs different statistical parameters to label the score of the extracted features. The feature score enables the selection optimal features for classification. The optimal features are further given to the Least Square- Support Vector Machine (LS-SVM) classifier for training and testing phases. The signals are acquired from public domain MIT-BIH arrhythmia database, used for validating the proposed technique for identifying the LVA using blood flow.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Amann, A., Tratnig, R., Unterkofler K., Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed. Eng. Online 4(60), 2005. https://doi.org/10.1186/1475-925X-4-60.

    Article  Google Scholar 

  2. Ghaffari, A., Golbayani, H., and Ghasemi, M., A new mathematical QRS detector using continues wavelet transform (CWT). Advances on Computer based Biological Signal Processing Techniques 34(2):81–91, 2008.

    Google Scholar 

  3. Aliya, S., Effects of vasodilation and arterial resistance on cardiac output. J Clinic Experiment Cardiol 2:170, 2011. https://doi.org/10.4172/2155-9880.1000170.

    Article  Google Scholar 

  4. Alonso-Atienza, F., Rojo-Álvarez, J. L., Rosado-Muñoz, A., Vinagre, J. J., García-Alberola, A., and Camps-Valls, G., Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection. Expert Syst. Appl. 39(2):1956–1967, 2012.

    Article  Google Scholar 

  5. Arafat, M., Chowdhury, A., and Hasan, M., A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram. SIViP 5:1–10, 2011.

    Article  Google Scholar 

  6. Arunasakthi, K., KamatchiPriya, L., and Askerunisa, A., Fisher Score Dimensionality Reduction for Svm Classification. In International Conference on Innovations in Engineering and Technology (ICIET14), pages 1900–1904. International Journal of Innovative Research in Science, Engineering and Technology.

  7. Aslantas, G., Gürgen, F., and Salah, A. A., GA-NN approach for ECG feature selection in rule based arrhythmia classification. Neural Network World 24:267, 2014.

    Article  Google Scholar 

  8. Koplan, B. A., and Stevenson, W. G., Ventricular tachycardia and sudden cardiac death. Mayo Clin. Proc. 84(3):289–297, 2009 Mayo Foundation for Medical Education and Research.

    Article  Google Scholar 

  9. Venkanna, C., and Raja Ganapati, B., Feature vector selection for automatic classification of ECG arrhythmias. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) 3(11):12840-12847. https://doi.org/10.15662/ijareeie.2014.0311007.

    Article  Google Scholar 

  10. Simioni, C., Nardozza, L. M. M., Araujo Junior, E., Rolo, L. C., Zamith, M., and Caetano, A. C., Heart stroke volume, cardiac output, and ejection fraction in 265 normal fetus in the second half of gestation assessed by 4D ultrasound using spatio-temporal image correlation. J. Matern. Fetal Neonatal. Med. 24:1159–1167, 2011. https://doi.org/10.3109/14767058.2010.545921 Download citation.

    Article  PubMed  Google Scholar 

  11. Dandona, L. et al., Nations within a nation: Variations in epidemiological transition across the states of India, 1990–2016 in the global burden of disease study. Lancet 390(10111):2437–2460, 2017.

    Article  Google Scholar 

  12. Anas, E., Lee, S., and Hasan, M., Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions. Biomed. Eng. Online 9(1):43, 2010.

    Article  Google Scholar 

  13. Roonizi, E. K., and Sassi, R., A signal decomposition model based Bayesian framework for ECG components separation. IEEE Trans. Signal Process. 64(3):665, 2016.

    Article  Google Scholar 

  14. Benjamin, E. J. et al., Heart disease and stroke statistics—2017 update: Summary. Circulation 135:e146–e603, 2017. https://doi.org/10.1161/CIR.0000000000000485.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Alonso-Atienza, F., Morgado, E., Fernández-Martínez, L., García-Alberola, A., and Rojo-Álvarez, J. L., Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans. Biomed. Eng. 61(3):832–840, 2014.

    Article  Google Scholar 

  16. Fukuta, H., and Little, W. C., The cardiac cycle and the physiologic basis of left ventricular contraction, ejection, relaxation, and filling. Heart Fail. Clin. 4:1–11, 2008.

    Article  Google Scholar 

  17. Hamde, S. T., Kumar, V., and Saxena, S. C., Feature extraction from ECG signals using wavelet transforms for disease diagnostics. Int. J. Syst. Sci. 33(13):1073–1085, 2002.

    Article  Google Scholar 

  18. Jiang, H., and Ching, W.-K., Correlation kernels for support vector machines classification with applications in cancer data. Computational and Mathematical Methods in Medicine 2012:Article ID 205025, 2012. https://doi.org/10.1155/2012/205025 Hindawi Publishing Corporation. 7 pages.

    Article  Google Scholar 

  19. Lance, G. N., and Williams, W. T., Computer programs for hierarchical polythetic classification. Comput. J. 9:60–64, 1966.

    Article  Google Scholar 

  20. Razavipour, F., Nikoo, M. h., and Abtahi, F., Numerical estimation of ejection fraction from 12-lead ECG. Journal of Bioengineering & Biomedical Science 6:1, 2015. https://doi.org/10.4172/2155-9538.1000172.

    Article  Google Scholar 

  21. Moody, G. B., and Mark, R. G., The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. 20(3):45–50, 2001.

    Article  CAS  Google Scholar 

  22. Rawther, N. N., and Cheriyan, J., Detection and classification of cardiac arrhythmias based on ECG and PCG using temporal and wavelet features. International Journal of Advanced Research in Computer and Communication Engineering 4(4):474, 2015. https://doi.org/10.17148/IJARCCE.2015.44108.

    Article  Google Scholar 

  23. Le, TQ., Bukkapatnam, S.T., Komanduri, R., Real-time lumped parameter modeling of cardiovascular dynamics using electrocardiogram signals: toward virtual cardiovascular instruments. IEEE Trans Biomed Eng. 2013;60(8):2350–2360.

    Article  Google Scholar 

  24. Scher, A. M., and Young, A. C., Ventricular depolarization and the genesis of QRS. Ann. N. Y. Acad. Sci. 65:768–778, 1957.

    Article  CAS  Google Scholar 

  25. Sedghamiz, Hooman. (2014). Matlab Implementation of Pan Tompkins ECG QRS detector. https://doi.org/10.13140/RG.2.2.14202.59841.

  26. Pan, J., and Tompkins, W. J., A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32(3):230–236, 1985.

    Article  Google Scholar 

  27. Sheldon, S. H., Gard, J. J., and Asirvatham, S. J., Premature ventricular contractions and non-sustained ventricular tachycardia: Association with sudden cardiac death, risk stratification, and management strategies. Indian Pacing and Electrophysiology Journal 10(8):357–371.

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology, Tiruchirappalli, India

    Hemalatha Karnan & N. Sivakumaran

  2. Department of Physiology Trichy, SRM Medical College Hospital & Research Centre, Irungalur, Tiruchirappalli, India

    Rajajeyakumar Manivel

Authors
  1. Hemalatha Karnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sivakumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajajeyakumar Manivel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hemalatha Karnan.

Ethics declarations

Conflict of interest

This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karnan, H., Sivakumaran, N. & Manivel, R. An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm. J Med Syst 43, 167 (2019). https://doi.org/10.1007/s10916-019-1312-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1312-7

Keywords

Search

Navigation