## 4 Results and Discussion
Figure 2 shows the time evolution, the Power Spectrum Density, and the modified 0-1 test results of PPG signals for five subjects chosen randomly from the 40 students, between 18 and 30 years old and a non-regular consumer of psychotropic substances, alcohol, or tobacco, according to project details [27,28]. Figure 2a-e show directly the PPG signals used for the study, although for visual clarity, only 5000 points of each PPG signal, which correspond to one minute of captured signal, are shown. In Figure 2f-j the PSD of the  
PPG signals is shown. Two prominent peaks are clearly visible in each PSD, related to the respiratory (lower frequency peak) and heart (higher frequency peak) rates, whose values, irrationally related, are indicated in bold text. Remember PPG fundamental frequency, typically around 1 Hz, depending on heart rate (0.5-4 Hz principal and first harmonic); and respiratory activity in roughly 0.2-0.35 Hz [47]. In any case, all PPG signals have been filtered to avoid motion artifacts as much as possible. The PSD shown for all the subjects in Figure 2f-j are quite similar, except for the relative amplitudes of the maximum peaks. Further, applying the 0-1 test, we observe in Figure 2k-o that for all of them, the diffusion model characterized by the graphic representation of \(q_{c}(n)\) versus \(p_{c}(n)\), is very similar to that displayed by a quasi-periodic signal, but of a spiral structure similar to a swirl with arms, whose orientation suggests an anti-clockwise or clockwise rotation, with the exception of the individual number 2, where the initial geometric structure seems to unfold into two spirals. In principle, this apparent splitting introduces greater complexity in its dynamics which, as it will be seen below, is reflected in a higher value of \(K_{m}\). On a small timescale, the \(K_{m}\) values for the different PPG signals tend to preserve the quasi-periodic nature (see Figure 2p, and Figure 2r-t) imposed by quasi-periodic modulation of the cardiorespiratory system. When the contribution of other physiological factors pushes the dynamics towards a more aperiodic behavior, as illustrated in Figure 2q, the regulation of blood volume is more effective depending on the physiological needs present and always conditioned by the psychophysical state in which the individual in question is. Hence, each individual externalizes a different \(K_{m}\) value within a range of normal operations. The results summarized in Table 2 also indicate the \(K_{m}\) trend, namely, the cumulative effect of \(K_{m}\) as the size of the data block increases, starting with a 5000-point data block and in 5000-point incremental steps. So \(K_{m}\) is calculated for an initial 5000-point data block, then 10,000, 15,000, and so on up to 60,000 points, taken from the mid-session of the signal acquisition phase. The parameter \(K_{m}\) trend allows evaluation of how \(K_{m}\) changes as more data are added in the computation of \(K_{m}\); so somehow, it enables the measurement of new operational modes (very low-frequency components of the PPG signal), as more minutes of the PPG signal are added to the \(K_{m}\) analysis. Based on the results of a small timescale analysis of the PPG signal in healthy young people, we can draw that most of the PPG signals have a quasi-periodic behavior [46] (see Figure 3a,b to a complete description). We do not omit to express that several publications provide for a clear linkage between biological signals and quasi-periodic behavior but not with an accurate definition of what is meant by quasi-periodicity property, such as mentioned [PERSON] and [PERSON] [48].