Skip to main content
SpringerLink
Log in

QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.

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

Similar content being viewed by others

References

  1. V. Gupta, M. Mittal, KNN and PCA classifier with autoregressive modelling during different ECG signal interpretation. J. Proc. Comput. Sci. 125, 18–24 (2018)

    Article  Google Scholar 

  2. V. Gupta, M. Mittal, in Respiratory Signal Analysis using PCA, FFT and ARTFA. 2016 International Conference on Electrical Power and Energy Systems (ICEPES-16) (Maulana Azad National Institute of Technology, Bhopal, India, 2016), pp. 221–225

  3. V. Gupta, M. Mittal, Electrocardiogram signals interpretation using Chaos theory. J. Adv. Res. Dyn. Control Syst. 10(2), 2392–2397 (2018)

  4. V. Gupta, M. Mittal, in Dimension Reduction and Classification in ECG Signal Interpretation Using FA & PCA: A Comparison. International Conference (M3HPCST-2018) (IPEC Ghaziabad, India, 2018) pp. 7–14

  5. https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/ecg-test. Accessed 17 Jan 2018

  6. V. Viknesh, P. Ram Prashanth, Matlab implementation of ECG signal processing. IOSR J. VLSI Signal Proc. 3(1), 40–47 (2013)

    Article  Google Scholar 

  7. J. Kranjec, Non-contact heart rate and heart rate variability measurements: a review. Elsevier. J. Bio. Sig. Proc. Conf. 13, 102–112 (2014)

    Google Scholar 

  8. A.P.M. Gorgels, Electrocardiographyy. J. Cardiol. Med. 8, 8–9 (2007). https://doi.org/10.1007/978-1-84628-715-2_3

    Article  Google Scholar 

  9. B.J. Drew, R.M. Califf, M. Funk, E.S. Kaufman, M.W. Krucoff, M.M. Laks, P.W. Macfarlane, C. Sommargren, S. Swiryn, G.F. Van Hare, Practice standards for electrocardiographic monitoring in hospital settings: an American heart association statement from the councils on cardiovascular nursing, clinical cardiology, and cardiovascular disease in the young: endorsed by the interna-tional society of computerized electrocardiology and the American Association of Critical-Care Nurses. Circulation 110(17), 2721–2746 (2004)

    Article  Google Scholar 

  10. Y. Kaya, H. Pehlivan, Feature selection using genetic algorithms for premature ventricular contraction classification, in 2015 9th IEEE International Conference (2015), pp. 1229–1232

  11. I. Kaur, R. Rajni, A. Marwaha, ECG signal analysis and arrhythmia detection using wavelet transform. J. Inst. Eng. India Ser. B 97(4), 499–507 (2016)

    Article  Google Scholar 

  12. J.P. Madeiro, P.C. Cortez, F.I. Oliveira, R.S. Siqueira, A new approach to QRS segmentation based on wavelet bases and threshold technique. Med. Eng. Phys. 29, 26–37 (2007)

    Article  Google Scholar 

  13. N.V. Thakor, J.G. Webster, W.J. Thompkins, Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. 31(11), 702–705 (1984)

    Article  Google Scholar 

  14. Z. Zidelmal, A. Amirou, M. Adnane, A. Belouchrani, QRS detection based on wavelet coefficients. J. Comput. Methods Progr. Biom. 107(3), 490–496 (2012)

    Article  Google Scholar 

  15. E.D. Übeyli, ECG beats classification using multiclass support vector machines with error correcting output codes. J. Dig. Signal Proc. 17(3), 675–684 (2007)

    Article  Google Scholar 

  16. R.B. Govindan, K. Narayanan, M.S. Gopinathan, On the evidence of deterministic chaos in ECG: surrogate and predictability Analysis. J. Chaos 8(2), 495–502 (1998)

    Article  Google Scholar 

  17. M. Casdagli, Chaos and deterministic versus stochastic nonlinear modeling. J. R. Stat. Soc. Ser B Methodol. 159(2), 1–23 (1991)

    Google Scholar 

  18. S. Sahoo, P. Biswal, T. Das, S. Sabut, De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding. Proc. Technol. 25, 68–75 (2016)

    Article  Google Scholar 

  19. I. Saini, QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res. 4(4), 331–344 (2013)

    Article  Google Scholar 

  20. M. Merino, I.M. Gómez, A.J. Molina, Envelopment filter and K-means for the detection of QRS waveforms in electrocardiogram. J. Med. Eng. Phys. 37(6), 605–609 (2015)

    Article  Google Scholar 

  21. R.J. Martis, U.R. Acharya, K.M. Mandana, A.K. Ray, C. Chakraborty, Cardiac decision making using higher order spectra. J. Biomed. Signal Proc. Control 8(2), 193–203 (2013)

    Article  Google Scholar 

  22. P. Kora, A. Annavarapu, P. Yadlapalli, K.S.R. Krishna, V. Somalaraju, ECG based atrial fibrillation detection using sequency ordered complex hadamard transform and hybrid firefly algorithm. J. Eng. Sci. Technol. 20(3), 1084–1091 (2017)

    Google Scholar 

  23. U.R. Acharya, O. Faust, N.A. Kadri, J.S. Suri, W. Yu, Automated identification of normal and diabetes heart rate signals using nonlinear measures. J. Comput. Biol. Med. 43(10), 1523–1529 (2013)

    Article  Google Scholar 

  24. B.S. Shaik, G.V.S.S.K.R. Naganjaneyulu, T. Chandrasheker, A.V. Narasimhadhan, A method for QRS delineation based on STFT using adaptive threshold. Proc. Comput. Sci. 54, 646–653 (2015)

    Article  Google Scholar 

  25. Y. Li, Heartbeat Detection, Classification and Coupling Analysis using Electrocardiography Data. Thesis, Doctor of Philosophy, Case Western Reserve University, 2014. https://etd.ohiolink.edu/!etd.send_file?accession=case1405084050&disposition=inline. Accessed 15 Dec 2018

  26. A.K. Dohare, V. Kumar, R. Kumar, An efficient new method for the detection of QRS in electrocardiogram. J. Comput. Electr. Eng. 40(5), 1–14 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  28. Physionet database/MIT-BIH Arrhythmia database. Accessed 17 Mar 2017

  29. https://www.rohde-schwarz.com/in/applications/capturing-small-ecg-signals-in-medical-applications-application-card_56279-152385.html. Accessed 17 Nov 2017

  30. C.H. Skiadas, C. Skiadas, Handbook of Applications of Chaos Theory, 1st edn. (CRC Press, Boca Raton, 2016), pp. 377–395

    MATH  Google Scholar 

  31. C. Wen, ECG Beat Classification Using GreyART Network, 1st edn. (IET Signal Process, Boca Raton, 2007), pp. 19–28

    Google Scholar 

  32. J.C. Sprott, Strange attractors: creating patterns in Chaos. Am. J. Phys. (2000). https://doi.org/10.1119/1.17885

    Article  Google Scholar 

  33. D.T. Kaplan, L. Glass, Direct test for determinism in a time series. Phys. Rev. Lett. 68(4), 427–430 (1992)

    Article  Google Scholar 

  34. F. Takens, Lectures Notes in Mathematics: Detecting Strange Attractor in Turbulence, in Dynamical Systems of Turbulence, ed.by D.A. Rand, B. S. Young, vol. 898 (Springer, Berlin, 1981), pp. 366–381

  35. R.J. Martis, U.R. Acharya, C.M. Lim, J.S. Suri, Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. J. Knowl. Based Syst. 45, 76–82 (2013)

    Article  Google Scholar 

  36. R.J. Martis, U.R. Acharya, K.M. Mandana, A.K. Ray, C. Chakraborty, Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. J. Expert Syst. Appl. 39(14), 11792–11800 (2012)

    Article  Google Scholar 

  37. R. Rodríguez, A. Mexicano, J. Bila, S. Cervantes, R. Ponce, Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J. Appl. Res. Technol. 13(2), 261–269 (2015)

    Article  Google Scholar 

  38. V. Gupta, G. Singh, M. Mittal, S.K. Pahuja, Fourier Transform of Untransformable Signals Using Pattern Recognition Technique, in Proceedings of the Second International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT’10) (IEEE Computer Society, Washington, DC, 2010), pp. 6–9

  39. M.P.S. Chawla, PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: a survey and comparison. J. Appl. Soft Comput. 11(2), 2216–2226 (2011)

    Article  Google Scholar 

  40. R.J. Martis, U.R. Acharya, H. Adeli, Current methods in electrocardiogram characterization. Comput. Biol. Med. 48, 133–149 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Varun Gupta

  2. National Institute of Technology, Kurukshetra, India

    Monika Mittal

Authors
  1. Varun Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Monika Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Varun Gupta.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, V., Mittal, M. QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases. J. Inst. Eng. India Ser. B 100, 489–497 (2019). https://doi.org/10.1007/s40031-019-00398-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-019-00398-9

Keywords

Search

Navigation