Short-Duration Monofractal Signals for Heart Failure Characterization Using CNN-ELM Models

Monofractal analysis offers a promising framework for characterizing cardiac dynamics, particularly in the early detection of heart failure. However, most existing approaches rely on long-duration physiological signals and do not explore the classification of disease severity. In this study, we propose a hybrid CNN-ELM model trained exclusively on synthetic monofractal time series of short length (128 to 512 samples), aiming to assess its ability to distinguish between healthy individuals and varying degrees of heart failure defined by the NYHA functional classification. Our results show that Hurst exponent distributions reflect the progressive loss of complexity in cardiac rhythms as heart failure severity increases. The model successfully classified both binary (healthy vs. sick) and multiclass (NYHA I–IV) scenarios by grouping Hurst exponent values (H0.1 to H0.9) into clinical categories, achieving peak accuracy ranges of 97.3–98.9% for binary classification and 96.2–98.8% for multiclass classification across signal lengths of 128, 256, and 512 samples. Importantly, the CNN-ELM architecture demonstrated fast training times and robust generalization, outperforming previous approaches based solely on support vector machines. These findings highlight the clinical potential of monofractal indices as non-invasive biomarkers of cardiovascular health and support the use of short synthetic signals for scalable, low-cost screening applications. Future work will extend this framework to multifractal and real-world clinical data and explore its integration into intelligent diagnostic systems.