TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Interpolation of electrocardiogram signals using Chebyshev polynomials optimization using genetic algorithms

* , ,

* Corresponding author · click or hover a name for details

pp. 1435–1444Vol. 27Issue 4June 2024DOI: 10.47974/JDMSC-1997 Crossmark XML
Published Online:
26 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1997
Pages:
1435–1444

Abstract

The research presented in this article specifically explores the utility of Chebyshev polynomials in electrocardiogram (ECG) signal processing and specializes in its correction and development capability. ECG sign evaluation is an important a part of coronary heart monitoring and is crucial for figuring out and monitoring heart issues. However, dependable analysis is hard, mainly while looking to come across small defects. Additionally, this observe presents a brand new technique for interpolation, smoothing and optimization of ECG statistics using Chebyshev polynomials. ECG features had been normalized to conform to the character of Chebyshev polynomials. Then the polynomial coefficients are calculated by way of the orthogonal projection technique. The aim of this look at is to reduce the mistake among the reconstructed ECG characteristics and the original ECG traits with the aid of optimizing the coefficients using a genetic algorithm (GA) in MATLAB. This strategy calls for collecting ECG statistics, segmenting it into person heartbeats the use of the Pan-Tompkins technique, the usage of a finite impulse reaction (FIR) bandpass filter as a preprocessing step, and ultimately applying Chebyshev polynomials for function extraction. The effects show how effective our technique is, showing desirable convergence of GA and lowering errors, for that reason offering an accurate and dependable illustration of ECG alerts that may be utilized in prognosis and monitoring

Keywords

ECG signal evaluationInterpolationSmoothingOptimizationPolynomial coefficientsOrthogonal projection techniqueError reductionGenetic algorithm (GA)MATLAB

Subject Classifications

Primary 46G20Secondary 65D05

References

[1] Gupta, V., Mittal, M., Mittal, V. et al. A Critical Review of Feature Extraction Techniques for ECG Signal Analysis. J. Inst. Eng. India Ser. B 102, pp. 1049–1060 (2021). https://doi.org/10.1007/s40031-021-00606-5.
[2] Xie, L., Li, Z., Zhou, Y., He, Y., & Zhu, J. Computational Diagnostic Techniques for Electrocardiogram Signal Analysis. Sensors, 20(21), pp. 6318 (2020). MDPI AG. Retrieved from http://dx.doi.org/10.3390/s20216318.
[3] U. Satija, B. Ramkumar and M. S. Manikandan, A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment,” in IEEE Reviews in Biomedical Engineering, 11, pp. 36-52, (2018). doi: 10.1109/RBME.2018.2810957.
[4] Selcan Kaplan Berkaya, Alper Kursat Uysal, Efnan Sora Gunal, Semih Ergin, Serkan Gunal, M. Bilginer Gulmezoglu, A survey on ECG analysis, Biomedical Signal Processing and Control, 43, pp. 216-235, ISSN 1746-8094, (2018). https://doi.org/10.1016/j.bspc.2018.03.003.
[5] Ahmed, A. F., & Al-Obaidi, M. K.. A review of ECG signal filtering approaches. Global Journal of Engineering and Technology Advances, 11(03), pp. 093-097, (2022).
[6] Jagannath, D. J., & Selvakumar, A. I.. Issues and research on foetal electrocardiogram signal elicitation. Biomedical signal processing and control, 10, pp. 224-244, (2014).
[7] Chaturvedi, R., & Yadav, Y.. Analysis of ECG signal by Polynomial Approximation. International Journal on Recent and Innovation Trends in Computing and Communication, 2(5), pp. 1029-1033, (2014).
[8] Yadav, O.P., Ray, S. ECG Signal Characterization Using Lagrange-Chebyshev Polynomials. Radioelectron.Commun.Syst. 62, pp. 72–85 (2019). https://doi.org/10.3103/S0735272719020031.
[9] K. Assaleh and H. Al-Nashash, “A novel technique for the extraction of fetal ECG using polynomial networks,” in IEEE Transactions on Biomedical Engineering, 52, 6, 1148-1152, (June 2005). doi: 10.1109/TBME.2005.844046.
[10] Gil, A., G Márquez, D., Caffarena, G., Iriarte, A., & Otero, A. GPU-Based Acceleration of ECG Characterization Using High-Order Hermite Polynomials. Current Bioinformatics, 11(4), pp. 430-439, (2016).
[11] Arora, R., Agrawal, A., Arora, R., Poonia, R. C., & Madaan, V. (2021). Prediction and forecasting of COVID-19 outbreak using regression and ARIMA models. Journal of Interdisciplinary Mathematics, 24(1), 227–243.
[12] Xu, Y., Liu, G., Hao, M. et al. Analysis of affective ECG signals toward emotion recognition. J. Electron. (China) 27, pp. 8–14 (2010). https://doi.org/10.1007/s11767-009-0094-3.

Views: 314Downloads: 118Citations: 0