Mathematical analysis of electrocardiograms for control signal development
DOI:
https://doi.org/10.5564/jase-a.v6i1.5362Keywords:
Electrocardiogram (ECG), signal processing, Fourier analysis, control signal modeling, biomedical instrumentationAbstract
Considering that the input signal of electrocardiogram (ECG) equipment must correspond to the characteristics and timing requirements of the control system, this study presents a mathematical analysis of ECG signals to determine an optimal control-signal model. Fourier-based signal modeling was applied to ECG wave forms using the MathCAD computational environment, with emphasis on accuracy, stability, and computational efficiency.
Several approximation orders were evaluated, and the eighth-order Fourier model was identified as providing the most favorable balance between signal fidelity and noise suppression. This model preserved key morphological features of the ECG waveform while minimizing redundancy, achieving an average reconstruction deviation of less than 3%.
The results demonstrate that Fourier-based mathematical modeling offers a reliable and efficient foundation for control-signal development in biomedical systems, supporting improved synchronization, real-time signal interpretation, and adaptive system design.
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References
1. Endeka, M.E.: The model of the conduction system of the heart and its use in automated cardiological and diagnostic complexes. PhD thesis synopsis (2010)
2. Mordvintseva, I.V., Polubinsky, V.L., Khazansky, S.I.: Information-technological procurement of a family doctor. In: Proceedings of the APEE-2010 International Conference, pp. 114–116 (2010)
3. Sörnmo, L., Laguna, P.: Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier Academic Press, London (2005). https://doi.org/10.1016/B978-012437552-9/50007-6
4. McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003). https://doi.org/10.1109/TBME.2003.808805
5. Martis, R.J., Acharya, U.R., Min, L.C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437–448 (2013). https://doi.org/10.1016/j.bspc.2013.01.005
6. Addison, P.S.: Wavelet transforms and the ECG: A review. Physiol. Meas. 26(5), R155–R199 (2005). https://doi.org/10.1088/0967-3334/26/5/R01
7. Clifford, G.D., Azuaje, F., McSharry, P.: Advanced Methods and Tools for ECG Data Analysis. Artech House, Boston (2006)
8. Inan, O.T., Giovangrandi, L., Kovacs, G.T.A.: Robust neural network-based classification of electrocardiograph signals. IEEE Trans. Biomed. Eng. 59(9), 2649–2657 (2012)
9. Pahlm, O., Sörnmo, L.: Software QRS detection in ambulatory monitoring a review. Med. Biol. Eng. Comput. 22(4), 289–297 (1984). https://doi.org/10.1007/BF02442095
10. Acharya, U.R., Fujita, H., Lih, O.S., et al.: Automated detection of coronary artery disease using different datasets with convolutional neural networks. Knowl.-Based Syst. 145, 34–45 (2018)
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