WAVELET-BISPECTRUM METHOD FOR DETERMINING QRS COMPLEX POSITIONS IN ECG SIGNALS

Authors

DOI:

https://doi.org/10.30888/2663-5712.2025-33-02-076

Keywords:

ECG signal, QRS complex, wavelet transform, Morlet wavelet, wavelet bispectrum

Abstract

The paper considers and investigates a new method for determining the positions of characteristic points of an electrocardiographic signal based on wavelet-bispectrum signal processing, which provides an increase in the probability of determining the R-c

References

Kontaxis, S., Lazaro, J., Corino, V. D. A., Sandberg, F., Bailon, R., Laguna, P., & Sornmo, L. (2020). ECG-Derived Respiratory Rate in Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, Vol.67(3), P.905–914.

DOI: https://doi.org/10.1109/tbme.2019.2923587

Lupenko, S., & Butsiy, R. (2024). Express method of biometric person authentication based on one cycle of the ECG signal. Scientific journal of the Ternopil national technical university, Vol.1(113), P.100–110.

DOI: https://doi.org/10.33108/visnyk_tntu2024.01.100

Sun, S., Bresch, E., Muehlsteff, J., Schmitt, L., Long, X., Bezemer, R., Paulussen, I., Noordergraaf, G. J., & Aarts, R. M. (2023). Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery. Biomedical Signal Processing and Control, 79, 104040.

DOI: https://doi.org/10.1016/j.bspc.2022.104040

Kumar, B., Soundararajan, R., Natesan, K., & Santhi, R. M. (2023). Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction. У RAiSE-2023. MDPI. Vol. 59, №1. P. 1-10.

DOI: https://doi.org/10.3390/engproc2023059126

Viunytskyi, O., & Shulgin, V. (2017). Signal processing techniques for fetal electrocardiogram extraction and analysis. У 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO). IEEE. P. 325–328.

DOI: https://doi.org/10.1109/elnano.2017.7939772

Shi, X., Yamamoto, K., Ohtsuki, T., Matsui, Y., & Owada, K. (2023). Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment. Bioengineering, 10(1), 66. P. 1-17.

DOI: https://doi.org/10.3390/bioengineering10010066

Ali, S. T. A., Kim, S., & Kim, Y.-J. (2024). Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection. Applied Sciences, Vol. 14, № 21. P. 1-18.

DOI: https://doi.org/10.3390/app142110078

Pan, J., & Tompkins, W. J. (1985). A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), № 3. P. 230-236.

DOI: https://doi.org/10.1109/tbme.1985.325532

Shulgin, V., & Viunytskyi, O. (2018). Spatio-temporal signal processing for fetus and mother state monitoring during pregnancy. У 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). IEEE. P. 677-680.

DOI: https://doi.org/10.1109/dessert.2018.8409210

Wang X. et al. PN-QRS: An Uncertainty-aware QRS-complex Detection Method for Wearable ECGs. // TechRxiv. 2022. P. 1-11.

DOI: https://doi.org/10.36227/techrxiv.21431673.v1

Yochum, M., Renaud, C., & Jacquir, S. (2016). Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control, Vol. 25. P. 46–52.

DOI: https://doi.org/10.1016/j.bspc.2015.10.011

Jamšek, J., Stefanovska, A., McClintock, P. V. E., & Khovanov, I. A. (2003). Time-phase bispectral analysis. Physical Review E, Vol. 68. P.1-12.

DOI: https://doi.org/10.1103/physreve.68.016201

Witte, H., Schack, B., Helbig, M., Putsche, P., Schelenz, C., Schmidt, K., & Specht, M. (2000). Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period. Journal of Physiology-Paris, Vol. 94. P. 427-434.

DOI: https://doi.org/10.1016/s0928-4257(00)01086-x

Newman, J., Pidde, A., & Stefanovska, A. (2021). Defining the wavelet bispectrum. Applied and Computational Harmonic Analysis, Vol. 51. P. 171-224.

DOI: https://doi.org/10.1016/j.acha.2020.10.005

Goupillaud, P., Grossmann, A., & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, Vol. 23, № 1. P. 85-102.

DOI: https://doi.org/10.1016/0016-7142(84)90025-5

Delprat, N., Escudie, B., Guillemain, P., Kronland-Martinet, R., Tchamitchian, P., & Torresani, B. (1992). Asymptotic wavelet and Gabor analysis: extraction of instantaneous frequencies. IEEE Transactions on Information Theory, Vol. 38. P. 644-664.

DOI: https://doi.org/10.1109/18.119728

Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine, Vol. 20, № 3.

P. 45-50.

DOI: https://doi.org/10.1109/51.932724

Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020). PTB-XL, a large publicly available electrocardiography dataset. Scientific Data, Vol. 7. P. 1–15.

DOI: https://doi.org/10.1038/s41597-020-0495-6

Maršánová, L., Němcová, A., Smíšek, R., Vítek, M., & Smital, L. (2019). Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts. Scientific Reports, 9(1), 19053.

DOI: https://doi.org/10.1038/s41598-019-55323-3

Gal Y., Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning // International Conference on Machine Learning. PMLR. 2016. P. 1050–1059.

Rahaman R. et al. Uncertainty quantification and deep ensembles // Advances in Neural Information Processing Systems. 2021. Vol. 34. P. 1-16.

DOI: https://doi.org/10.48550/arXiv.2007.08792

Published

2025-09-30

How to Cite

В’юницький, О., & Тоцький, О. (2025). WAVELET-BISPECTRUM METHOD FOR DETERMINING QRS COMPLEX POSITIONS IN ECG SIGNALS. SWorldJournal, 2(33-02), 176–191. https://doi.org/10.30888/2663-5712.2025-33-02-076

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