Review on Graph Theory based Optimization methods for Heart Rate Analysis
Keywords:
Heart rate variability, Electrocardiogram, Heart disease diagnosis, Complex networks, Machine learningAbstract
This review paper explores the intersection of graph theory and heart rate analysis for the identification of heart diseases. The paper investigates how graph-based representations of heart rate variability (HRV) and electrocardiogram (ECG) signals can enhance diagnostic capabilities through optimization techniques. We discuss various graph theoretical measures, network construction methods, and machine learning integration approaches that have emerged in recent literature. The review highlights how these methods have improved the accuracy, efficiency, and interpretability of heart disease detection systems. We also address current challenges and future research directions, emphasizing the potential for graph-based methods to revolutionize cardiac care through more personalized and precise diagnostic tools. This comprehensive analysis demonstrates that graph theory provides a powerful mathematical framework for capturing the complex temporal and structural relationships in cardiac signals that can significantly enhance heart disease identification.
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