MATHEMATICAL MODELING OF HEART RHYTHM: ECG SIGNAL ANALYSIS
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Abstract
Electrocardiogram (ECG) signal analysis through mathematical modeling has become an essential tool in modern cardiology for the accurate assessment of heart rhythms. This paper presents a comprehensive overview of methods employed in preprocessing, feature extraction, and classification of ECG signals to identify normal and abnormal cardiac activities. The integration of time-domain, frequency-domain, and nonlinear dynamic features combined with machine learning algorithms enhances the detection and diagnosis of arrhythmias. The results demonstrate that advanced mathematical models significantly improve the reliability and efficiency of automated heart rhythm analysis, contributing to better clinical decision-making and patient care.
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1.Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences, 405, 81–90.
2.Addison, P. S. (2005). Wavelet transforms and the ECG: A review. Physiological Measurement, 26(5), R155–R199.
3.Clifford, G. D., Azuaje, F., & McSharry, P. E. (2006). Advanced methods and tools for ECG data analysis. Artech House.
4.Martis, R. J., Acharya, U. R., & Mandana, K. M. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437–448.
5.Phinyomark, A., Petcharat, T., & Limsakul, C. (2018). Feature extraction and selection for emotion recognition from EEG signals. Sensors, 18(8), 2619.