## 1 Introduction  
A physiological system is limited to the field of dissipative dynamic systems since energy dissipation results are extremely useful to stabilize biological dynamics, ensuring proper functioning under optimal conditions of a living organism [1]. In the context of dissipative dynamic systems, recurrence properties and ergodicity facilitate the analysis of physical processes from an only scalar time series, which is nothing other than a sequence of measurements of an observable, regular procedure in practice or of several observables of a physical system, sampled at regular time intervals. In the case that concerns us, we study a sequence of measurements of an observable, the PPG signal. The PPG signal takes its name from the optical technique, called photoplethysmogram, applied for volumetric analysis of an organ (plethysmogram) or from the device used to measure it, the photoplethysmograph, introduced by [PERSON] in 1937 [2]. This signal is also known as a peripheral pulse wave, mainly employed in clinical settings with the aid of a peripheral pulse monitor or pulse oximeter [3]. The PPG signal, while seemingly simple, contains sluggish dynamic variations that make it an extraordinarily complex signal. These dynamic subtleties blurried by artifacts like motion artifacts or baseline drift [4, 5] share the same low-frequency range. The PPG signal contains relevant physiological information that is not limited to heart rate or blood oxygen saturation, indicators that are broadly representative of clinical settings [6]. The application of classical filtering techniques is restricted because they remove some of this relevant physiological information. To overcome this, especially critical in wearable PPG devices [7; 8], new sophisticated denoising techniques have been successfully developed [9; 10; 11; 12] but are beyond the scope of this paper. Self-care today, a culture that improves the quality of life and promotes the sustainability of the healthcare system, encourages the widespread use of wearable pulse oximeters for home healthcare [13], particularly in the last year, during the COVID-19 emergency. New generations of pulse oximeters, such as contactless pulse oximeters, aim at a better ergonomic fitting to the daily events of day-to-day life. The medical device industry is committed to getting out of the hospital and improving the population's quality of life through personal self diagnosis at home [14; 15]. Hat new pulse oximeters and new PPG-based biomedical applications will likely emerge in the near future with a more thorough screening of the patient's physiological condition. In epidemic events, their capacity to identify patterns discriminating between healthy and infected people could be exploited, thus limiting the contagion risk. This would easily prevent the healthcare system collapse [16]. Accordingly, a better understanding of the PPG signal dynamics would reveal the intricate physiological mechanisms involved in the cardiovascular system, facilitating the implementation of biomedical applications for the early diagnosis of possible cardiorespiratory pathologies with more accuracy. Between the different dynamic behaviors that can be present in the dynamical systems theory, chaos is the most complex and representative when the evolution is nonlinear [17; 18; 19]. Chaos is a deterministic behavior. One way to extract information from a system is to analyze the deterministic degree of its behavior, being able to identify if the dynamic is regular, either periodic or quasi-periodic, or more erratic, being chaotic or even non-deterministic, such as random (stochastic) [20]. Several studies have contributed to analyzing a PPG signal from a nonlinear point of view, being representative of the study conducted by [PERSON] et al. [21], the stochastic model by [PERSON] et al. [22], and the work of [PERSON] et al. [23]. In 2004, [PERSON] and [PERSON] proposed a new method to test chaos in a deterministic system, the 0-1 test [24].