A Survey on Signal processing tools for the analysis of heart rate variation: An approach for the early diagnosis and management of cardiovascular disease

Heart rate variability (HRV) is a non-invasive, reliable, and reproducible biomarker of autonomic nervous system (ANS) function. This review provides a critical and comparative analysis of the primary HRV evaluation methods (temporal, frequency, non-linear, and geometric) as applied to the early diagnosis and management of cardiovascular, metabolic, psychiatric, and neurological pathologies. We place particular emphasis on evaluating the accuracy, robustness, and clinical applicability of these methods. Recent advances in signal processing and artificial intelligence (AI) are paving the way for more precise real-time detection tools using portable devices suitable for telemedicine. However, challenges such as protocol heterogeneity, the confounding effect of heart rate, data imbalance in AI models, and a lack of standardization still limit large-scale clinical deployment. This study not only highlights the promising prospects of integrating HRV into personalized prevention strategies but also provides a critical discussion of methodological challenges and suggests avenues for future improvement, including the integration of explainable AI and robust validation frameworks.