MATHEMATICAL MODELING OF HEART RHYTHM: ECG SIGNAL ANALYSIS
DOI:
https://doi.org/10.55640/Keywords:
ECG signal, heart rhythm, mathematical modeling, signal processing, feature extraction, arrhythmia detection, machine learning, deep learning.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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