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Description
This study investigates the presence of deterministic chaos in human cardiac dynamics by analyzing electrocardiographic (ECG) time series through the framework of nonlinear dynamics. Although the heartbeat exhibits a macroscopic rhythm, secondary fluctuations display complex, irregular patterns characteristic of non-linear systems. The scalar time series was analyzed using spectral decomposition and phase-space reconstruction techniques, relying on Takens' embedding theorem to recover the underlying strange attractor. The optimal embedding dimension was determined via the False Nearest Neighbors (FNN) algorithm, indicating a high-dimensional deterministic structure requiring at least six dimensions. Furthermore, the local instability and sensitivity to initial conditions—a hallmark of chaotic systems—were quantified by estimating the maximal Lyapunov exponent using Rosenstein's algorithm, yielding a positive value (1.5). Importantly, the methodology employed here is entirely general and can be directly translated to the analysis of any complex observational time series—ranging from cardiovascular fluctuations to geophysical and seismological data. These results demonstrate that complex signals often exhibit signatures of low-dimensional deterministic chaos rather than pure stochastic noise, providing a versatile framework for studying critical dynamics and synchronization phenomena across different physical systems.