Advancing entropy analysis for heart rate variability: clinical insights for aging and diabetes

Heart rate variability (HRV) entropy analysis is an emerging tool for assessing autonomic function, particularly in older and diabetic populations. Traditional HRV metrics, limited in capturing signal complexity, have been supplemented by advanced entropy methods such as multi-scale entropy (MSE), permutation entropy (PermEn), and the baroreflex entropy index (BEI). In this review, we introduce novel entropy indices, including percussion entropy (PercEn), and explore their potential to enhance HRV assessment. Additionally, we discuss the integration of novel data sources, such as pulse wave velocity (PWV) and crest time, offering an in-depth evaluation of autonomic dysfunction. Performance metrics such as classification accuracy (up to 92.5% in diabetic autonomic dysfunction prediction), sensitivity (87.3%), and specificity (89.1%) demonstrate the potential clinical utility of entropy-based HRV analysis. The key challenges include the prognostic value of entropy metrics, the impact of confounding factors, and the need for standardised methodologies. Advances in machine learning and wearable technology are also examined for real-time HRV monitoring and personalised healthcare. The findings highlight entropy analysis as a promising avenue for autonomic dysfunction assessment, with future research needed to optimise methodologies and establish clinical validation.