This chapter introduces the two theoretical frameworks employed to analyze noise in physiological signals: probability theory and dynamical systems theory. The first framework—probability theory—revisits fundamental concepts such as random variables and probability distributions, along with their key properties. Particular attention is given to stochastic processes, which are essential for modeling random phenomena. Within this context, the focus is on stationary processes, defined as those whose statistical characteristics (e.g., mean and variance) remain constant over time. The second framework—dynamical systems theory—addresses the deterministic aspects of systems evolving in metric spaces equipped with probability measures. Key features of deterministic dynamics are introduced, including sensitive dependence on initial conditions, chaos, and the concept of attractors. The chapter also outlines the principal quantifiers used in the analysis of dynamical systems, with a particular emphasis on Kolmogorov–Sinai (K–S) entropy and its practical approximation for finite time series, namely Approximate Entropy (ApEn).