Entropy Analysis of Electrocardiography and Heart Rate Variability in Heart Failure: A Methodology-Oriented Narrative Review and Illustrative Evidence Map

Entropy measures characterize complex dynamics in electrocardiography (ECG) and heart rate variability (HRV), but their interpretation depends on estimator, signal representation, and analytical configuration. This methodology-oriented narrative review organized 126 references by evidentiary function and applied role-appropriate interpretation boundaries. Within this evidence set, 53 human heart-failure (HF) or HF-relevant reports underwent structured mapping by method family (template, multiscale, symbolic/distributional, spectral/wavelet, or conditional/coupling), research task, data provenance, and validation design. Findings varied with configuration, rhythm, preprocessing, and recording length, while reused datasets or shared cohorts limited the independence of report-level evidence. Of 12 reports presenting diagnostic/classification performance, only two reported participant-separated internal evaluation, without establishing a fully nested leakage-free pipeline. A non-exclusive set of 10 reports examined clinical outcomes or clinically relevant event associations, all within single cohorts. Neither subset included independent external patient-cohort validation or identified calibration. Entropy should therefore be treated as a configuration-, representation-, rhythm-, and task-specific research tool for dynamic phenotyping or a candidate component of multivariable models, rather than a standalone clinical measure. Progress requires complete estimator reporting and design-appropriate participant separation; candidates intended for prediction or decision support additionally require appropriate comparators, external validation, calibration, and decision-relevant evaluation.