Skip to main content
SpringerLink
Log in

Delineation of QRS features and denoising of ECG signal using Fejer Korovkin wavelet

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Cardiovascular diseases are a leading cause of increased mortality worldwide. Electrocardiogram (ECG) signals is the most effective tool and plays a crucial role in diagnosing heart conditions. The detection of R peaks within the QRS complex is pivotal for accurate heart condition diagnosis. However, denoising ECG signals and accurately detecting R peaks present significant challenges due to various noise factors. This research paper proposes an efficient R peak detection technique that leverages the Maximal Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fejer–Korovkin (FK) wavelet function. The proposed methodology is rigorously validated using standard physionet databases. The results of the proposed algorithm demonstrate exceptional performance for the TWADB and QTDB databases. For TWADB and QTDB, a sensitivity (Se+) of 99.97%, a positive prediction (P+) rate of 99.64%, a low detection error rate (DER) of 0.04 and QTDB, a Se+ of 99.95%, a P+ rate of 99.99%, and an extremely low DER of 0.063 has been achieved, respectively.

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

Availability of data and materials

On the request

Code availability

Available on the request.

References

  1. Singh PN, Mahapatra RP (2023) A novel deep learning approach for arrhythmia prediction on ecg classification using recurrent cnn with gwo. Int J Inform Technol, 16:1–9

  2. Mahajan P, Kaul A (2023) Optimized multi-stage sifting approach for ecg arrhythmia classification with shallow machine learning models. Int J Inform Technol, 16:1–16

  3. Sharma LD, Sunkaria RK (2021) Detection and delineation of the enigmatic u-wave in an electrocardiogram. Int J Inform Technol 13:2525–2532

    Google Scholar 

  4. Rao BM, Kumar A, Bachwani N, Marwaha P (2023) Detection of atrial fibrillation based on Stockwell transformation using convolutional neural networks. Int J Inform Technol 15:1937–1947

    Google Scholar 

  5. Chen H, Maharatna K (2020) An automatic r and t peak detection method based on the combination of hierarchical clustering and discrete wavelet transform. IEEE J Biomed Health Inform 24:2825–2832

    Article  Google Scholar 

  6. Cao H, Peyrodie L et al (2023) Variational mode decomposition-based simultaneous r peak detection and noise suppression for automatic ecg analysis. IEEE Sens J 23:8703–8713

    Article  Google Scholar 

  7. Venkatesh C, Lavanya M, Naga Swetha P, Naganjaneyulu M, Mohan Kumar Reddy K (2023) A neural network-based cardiovascular disease detection using ecg signals. Springer, pp 291–304

    Google Scholar 

  8. Mondhe D (2022) Cardiovascular disease detection using machine learning. Springer, pp 243–252

    Google Scholar 

  9. Belkadi MA, Daamouche A (2021) A robust qrs detection approach using stationary wavelet transform. Multimed Tools Appl 80:22843–22864

    Article  Google Scholar 

  10. Sharma N, Sunkaria RK, Sharma LD (2022) Qrs complex detection using stationary wavelet transform and adaptive thresholding. Biomed Phys Eng Express 8:065011

    Article  Google Scholar 

  11. Wang H et al (2022) Qrs detection of ecg signal using u-net and dbscan. Multimed Tools Appl 81:13319–13333

    Article  Google Scholar 

  12. Guendouzi F, Attari M (2022) Qrs complex detection in ecg signals using empirical wavelet transform and flower pollination algorithm. Period Polytech Electr Eng Comput Sci 66:380–390

    Article  Google Scholar 

  13. Mansourian N, Sarafan S, Torkamani-Azar F, Ghirmai T, Cao H (2023) Novel qrs detection based on the adaptive improved permutation entropy. Biomed Signal Process Control 80:104270

    Article  Google Scholar 

  14. Karakulak E (2023) Adaptive thresholding based low complexity qrs detection algorithm. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25:78–89

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Laguna P, Mark RG, Goldberg A, Moody GB (1997) A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg, In: Computers in Cardiology. IEEE pp 673–676. https://doi.org/10.1109/CIC.1997.648140

  17. Moody G (2008) The physionet/computers in cardiology challenge 2008: T-wave alternans, pp 505–508

  18. Roushangar K, Dolatshahi M, Alizadeh F (2023) Modwt and wavelet coherence-based analysis of groundwater levels changes detection. Paddy Water Environ 21:59–83

    Article  Google Scholar 

  19. Pan J, Tompkins WJ (1985) A real-time qrs detection algorithm. IEEE Trans Biomed Eng, 32:230–236

  20. Lu X, Pan M, Yu Y (2018) Qrs detection based on improved adaptive threshold. J Healthc Eng

  21. Zalabarria U, Irigoyen E, Martinez R, Lowe A (2020) Online robust r-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm. Appl Math Comput 369:124839

    MathSciNet  Google Scholar 

  22. Modak S, Taha LY, Abdel-Raheem E (2021) A novel method of qrs detection using time and amplitude thresholds with statistical false peak elimination. IEEE Access 9:46079–46092

    Article  Google Scholar 

  23. Zhao K, Li Y, Wang G, Pu Y, Lian Y (2021) A robust qrs detection and accurate r-peak identification algorithm for wearable ecg sensors. SCIENCE CHINA Inf Sci 64:182401

    Article  MathSciNet  Google Scholar 

  24. Pandit D et al (2017) A lightweight qrs detector for single lead ecg signals using a max-min difference algorithm. Comput Methods Programs Biomed 144:61–75

    Article  Google Scholar 

  25. Elgendi M (2013) Fast qrs detection with an optimized knowledge-based method: evaluation on 11 standard ecg databases. PLoS One 8:e73557

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Author notes

  1. Demissie Jobir Gelmecha, Ram Sewak Singh have contributed equally to this work.

Authors and Affiliations

  1. Electronics & Communication Engineering Department, School of Electrical Engineering & Computing, Adama Science and Technology University, Adama, 1888, Ethiopia

    Henok Mezemr Besfat, Demissie Jobir Gelmecha & Ram Sewak Singh

Authors
  1. Henok Mezemr Besfat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Demissie Jobir Gelmecha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ram Sewak Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally to prepare this manuscript

Corresponding author

Correspondence to Demissie Jobir Gelmecha.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Besfat, H.M., Gelmecha, D.J. & Singh, R.S. Delineation of QRS features and denoising of ECG signal using Fejer Korovkin wavelet. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01804-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41870-024-01804-2

Keywords

Search

Navigation