Skip to main content
SpringerLink
Log in

P and T wave detection and delineation of ECG signal using differential evolution (DE) optimization strategy

  • Technical Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Generally, P and T waves in an electrocardiogram (ECG) signal are lower in amplitude compared to amplitude of QRS complex and contaminated with noises from various sources. Due to these problems and lack of universal delineation rule, the automated detection and delineation of T and P waves (on, off, and peak position of T and P wave) in the ECG signal are challenging task. The effectiveness for detection of on, off, and peak position of T and P wave by using differential evolution (DE) algorithm with the denoising technique has been verified in this manuscript. The denoising operation of the ECG signal has been performed by extended Kalman smoother (EKS) framework. DE algorithm is used for selection of optimized width and phase of five waves of the ECG signal. These parameters are used in EKS for initialization of the process noise covariance matrix and also development of the state equation. The new algorithm (an intelligent process of searching and subtraction) for detection of on, off and peak location of P and T waves without using amplitude threshold is developed by using the optimized parameters computed by the DE algorithm and denoised ECG signal with the help of the EKS framework. The effectiveness of the proposed technique has been validated using real-time QT database. Our proposed method shows better sensitivity, predicitvity and accuracy compared to other well-known methods for detection of on, off, peak location of P and T wave.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Rakshit M, Panigrahy D, Sahu PK (2016) An improved method for R-peak detection by using Shannon energy envelope. Sadhana 41:469–477. https://doi.org/10.1007/s12046-016-0485-8

    Google Scholar 

  2. Zhang Y, Wei S, Di Maria C, Liu C (2016) Using Lempel-Ziv Complexity to Assess ECG Signal Quality. J Med Biol Eng 36:625–634. https://doi.org/10.1007/s40846-016-0165-5

    Article  PubMed  PubMed Central  Google Scholar 

  3. Pereira T, Correia C, Cardoso J (2015) Novel methods for pulse wave velocity measurement. J Med Biol Eng 35:555–565. https://doi.org/10.1007/s40846-015-0086-8

    Article  PubMed  PubMed Central  Google Scholar 

  4. Panigrahy D, Rakshit M, Sahu PK (2016) FPGA implementation of heart rate monitoring system. J Med Syst 40:1–12. https://doi.org/10.1007/s10916-015-0410-4

    Article  Google Scholar 

  5. PUrerfellner H, Pokushalov E, Sarkar S et al (2014) P-wave evidence as a method for improving algorithm to detect atrial fibrillation in insertable cardiac monitors. Hear Rhythm 11:1575–1583. https://doi.org/10.1016/j.hrthm.2014.06.006

    Article  Google Scholar 

  6. Elgendi M, Eskofier B, Abbott D (2015) Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors 15:17693–17714. https://doi.org/10.3390/s150717693 (Switzerland)

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rakshit M, Panigrahy D, Sahu PK (2015) EKF with PSO technique for delineation of P and T wave in electrocardiogram (ECG) signal. 2015 2nd international conference on signal processing and integrated network. IEEE, pp 696–701

  8. Thakor NV, Zhu YS (1991) Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng 38:785–794. https://doi.org/10.1109/10.83591

    Article  CAS  PubMed  Google Scholar 

  9. Laguna P, Jané R, Caminal P (1994) Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res 27:45–60. https://doi.org/10.1006/cbmr.1994.1006

    Article  CAS  PubMed  Google Scholar 

  10. Martinez JP, Almeida R, Olmos S et al (2004) A wavelet-based ECG delineator evaluation on standard databases. IEEE Trans Biomed Eng 51:570–581. https://doi.org/10.1109/TBME.2003.821031

    Article  PubMed  Google Scholar 

  11. Mochimaru F (2002) Detecting the fetal electrocardiogram by wavelet theory-based methods. Prog Biomed Res 7:185–193

    Google Scholar 

  12. Trahanias P, Skordalakis E (1990) Syntactic pattern recognition of the ECG. IEEE Trans Pattern Anal Mach Intell 12:648–657. https://doi.org/10.1109/34.56207

    Article  Google Scholar 

  13. Dumont J, Hernández AI, Carrault G (2010) Improving ECG beats delineation with an evolutionary optimization process. IEEE Trans Biomed Eng 57:607–615. https://doi.org/10.1109/TBME.2008.2002157

    Article  Google Scholar 

  14. Lin C, Mailhes C, Tourneret JY (2010) P- and T-wave delineation in ECG signals using a bayesian approach and a partially collapsed gibbs sampler. IEEE Trans Biomed Eng 57:2840–2849. https://doi.org/10.1109/TBME.2010.2076809

    Article  PubMed  Google Scholar 

  15. Sayadi O, Shamsollahi MB (2009) A model-based Bayesian framework for ECG beat segmentation. Physiol Meas 30:335–352. https://doi.org/10.1088/0967-3334/30/3/008

    Article  CAS  PubMed  Google Scholar 

  16. Lin C, Kail G, Giremus A et al (2014) Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: block Gibbs sampler and marginalized particle filter. Sig Process 104:174–187. https://doi.org/10.1016/j.sigpro.2014.03.011

    Article  Google Scholar 

  17. Lenis G, Pilia N, Oesterlein T et al (2016) P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference. Biomed Tech 61:37–56. https://doi.org/10.1515/bmt-2014-0161 (Berl)

    Article  Google Scholar 

  18. Dubois R, Maison-Blanche P, Quenet B, Dreyfus G (2007) Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators. Comput Methods Programs Biomed 88:217–233. https://doi.org/10.1016/j.cmpb.2007.09.005

    Article  PubMed  Google Scholar 

  19. Panigrahy D, Sahu PK (2016) Extended Kalman smoother with differential evolution technique for denoising of ECG signal. Australas Phys Eng Sci Med 39:783–795. https://doi.org/10.1007/s13246-016-0468-4

    Article  CAS  PubMed  Google Scholar 

  20. Sameni R (2008) Extraction of fetal cardiac signals from an array of maternal abdominal recordings. Ph.D. thesis, Sharif University of Technology—Institut National Polytechnique de Grenoble

  21. Panigrahy D, Sahu PK (2015) Extraction of fetal electrocardiogram (ECG) by extended state Kalman filtering and adaptive neuro-fuzzy inference system (ANFIS) based on single channel abdominal. Sadhana 40:1091–1104

    Article  Google Scholar 

  22. Panigrahy D, Rakshit M, Sahu PK (2015) An efficient method for fetal ECG extraction from single channel abdominal ECG. In: 2015 international conference on industrial instrumentation and control (ICIC), pp 1083–1088

  23. Niknazar M, Rivet B, Jutten C (2013) Fetal ECG extraction by extended state Kalman filtering based on single-channel recordings. IEEE Trans Biomed Eng 60:1345–1352. https://doi.org/10.1109/TBME.2012.2234456

    Article  PubMed  Google Scholar 

  24. McSharry PE, Clifford GD, Tarassenko L, Smith LA (2003) A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng 50:289–294. https://doi.org/10.1109/TBME.2003.808805

    Article  PubMed  Google Scholar 

  25. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13:398–417. https://doi.org/10.1109/TEVC.2008.927706

    Article  Google Scholar 

  26. Panigrahy D, Sahu PK (2016) Extended Kalman smoother with differential evolution technique for denoising of ECG signal. Australas Phys Eng Sci Med. https://doi.org/10.1007/s13246-016-0468-4

    PubMed  Google Scholar 

  27. Storn R (1996) On the usage of differential evolution for function optimization. North American Fuzzy Information Processing Society NAFIPS’96, pp 519–523. https://doi.org/10.1109/NAFIPS.1996.534789

  28. Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). J Hered 104:542

    Google Scholar 

  29. Sameni R, Shamsollahi MB, Jutten C, Clifford GD (2007) A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng 54:2172–2185. https://doi.org/10.1109/TBME.2007.897817

    Article  PubMed  Google Scholar 

  30. Welch G, Bishop G (2006) An introduction to the Kalman filter. University of North Carolina at Chapel Hill, Chapel Hill, pp 1–16

    Google Scholar 

  31. Laguna P, Mar RG, Goldberg A, Moody GB (1997) A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol 24:673–676

    Google Scholar 

  32. Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20:45–50

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Author would like to thank editor and reviewers of the manuscript for valuable suggestion to improve the manuscript.

Funding

The authors certify they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, NIT Rourkela, Rourkela, Odisha, India

    D. Panigrahy & P. K. Sahu

Authors
  1. D. Panigrahy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. K. Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Panigrahy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human and animal rights

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panigrahy, D., Sahu, P.K. P and T wave detection and delineation of ECG signal using differential evolution (DE) optimization strategy. Australas Phys Eng Sci Med 41, 225–241 (2018). https://doi.org/10.1007/s13246-018-0629-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-018-0629-8

Keywords

Search

Navigation