This study investigates the multifractal nature of healthy human heartbeat dynamics and its loss in congestive heart failure. The researchers analyze heartbeat time series using multifractal analysis, which requires multiple exponents to characterize scaling properties. They find that healthy heart rate data exhibit multifractal behavior, characterized by a nonlinear scaling exponent τ(q), while heart failure data show a loss of multifractality, with τ(q) approaching linearity and a narrow range of exponents h. The multifractal properties are encoded in the Fourier phases of the heartbeat data, and the analysis reveals that healthy heartbeats have anti-correlated behavior, while heart failure exhibits less anti-correlation. The study compares healthy and heart failure data using multifractal analysis, finding that healthy subjects have a broader range of local Hurst exponents h, indicating more complex dynamics. The multifractal approach robustly discriminates between healthy and heart failure subjects, as shown in the results. The researchers also test the robustness of their method by analyzing a separate database of heartbeat data, successfully separating healthy and heart failure subjects. The multifractal analysis of heartbeat data provides a more detailed understanding of the complexity of healthy heart dynamics compared to traditional power spectrum analysis, which only captures single scaling exponents. The study also shows that the multifractal properties of healthy heartbeats are related to nonlinear features of the dynamics, as demonstrated by tests with surrogate time series. These results suggest that the multifractal behavior of healthy heartbeats is due to the complex interactions of feedback loops in the cardiac control system, which are disrupted in heart failure. The findings have implications for understanding the complexity of physiological systems and improving the diagnosis of heart conditions.This study investigates the multifractal nature of healthy human heartbeat dynamics and its loss in congestive heart failure. The researchers analyze heartbeat time series using multifractal analysis, which requires multiple exponents to characterize scaling properties. They find that healthy heart rate data exhibit multifractal behavior, characterized by a nonlinear scaling exponent τ(q), while heart failure data show a loss of multifractality, with τ(q) approaching linearity and a narrow range of exponents h. The multifractal properties are encoded in the Fourier phases of the heartbeat data, and the analysis reveals that healthy heartbeats have anti-correlated behavior, while heart failure exhibits less anti-correlation. The study compares healthy and heart failure data using multifractal analysis, finding that healthy subjects have a broader range of local Hurst exponents h, indicating more complex dynamics. The multifractal approach robustly discriminates between healthy and heart failure subjects, as shown in the results. The researchers also test the robustness of their method by analyzing a separate database of heartbeat data, successfully separating healthy and heart failure subjects. The multifractal analysis of heartbeat data provides a more detailed understanding of the complexity of healthy heart dynamics compared to traditional power spectrum analysis, which only captures single scaling exponents. The study also shows that the multifractal properties of healthy heartbeats are related to nonlinear features of the dynamics, as demonstrated by tests with surrogate time series. These results suggest that the multifractal behavior of healthy heartbeats is due to the complex interactions of feedback loops in the cardiac control system, which are disrupted in heart failure. The findings have implications for understanding the complexity of physiological systems and improving the diagnosis of heart conditions.