# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 4 Discussion
These methods are standardized and they can provide objective information regarding the condition of the cardiovascular system of the subjects studied. Much of the values of the studied Time-Domain and Frequency-Domain parameters (Tables 5 and 6) are statistically significant, since the p-value is less than 0.05. Therefore, these methods can distinguish the subjects of the two study groups. The nonlinear analysis of the PP interval series allows obtaining additional information regarding the subjects studied. Based on the results of the nonlinear analysis of the subjects studied, the following conclusions can be drawn:  
* Poincare plot allows for visual analysis of the entire recording of PP intervals based on the following parameters: the pattern of PP intervals, the ellipse size, and the points symmetry with respect to the identity line. This type of analysis makes it possible to distinguish healthy individuals from patients with various diseases. Figure 8 shows that the PP intervals for a healthy subject is comet-shaped and, for the patient with arrhythmia, has a fan pattern. Similar results are shown in a publication [30]. The values of the parameters SD1, SD2 of the ellipse, as well as SD1/SD2, are smaller for the group of patients with arrhythmia when compared to the group of healthy subjects (Table 7). Healthy subjects have a well-defined ellipse, while, in patients with arrhythmia, the ellipse approaches the shape of a circle. The graphs corresponding to the healthy individual and the patient with arrhythmia shown in Figure 8 are symmetrical to the identity line, which suggested that they have no rhythmic disturbance. Poincare plot analysis can distinguish between the two groups studied, because the p-value is less than 0.05. * The DFA method determines the values of the following parameters: Alpha 1-short-range scaling exponent; Alpha 2-long-range scaling exponent. These values are in the range of 1.0 to 1.5 for the two study groups, which is evidence of the fractal behavior of the PP interval series. * The MFDFA allows for new estimates to be obtained that give additional insight into the nonlinear, dynamic processes in the studied PPG signals. Figure 9 shows the multifractal spectrum and the behavior of the genaralized Hurst exponent of the two subjects studied: a healthy subject and a patient with arrhythmia. The healthy subject's multifractal spectrum is 2.2 times broader than that of the arrhythmatic patient. This is evidence that the PP interval series of the healthy subject have multifractal behavior and the patient with arrhythmia has monofractal behavior. The Genaralized Hurst exponent reflects the non-homogeneous structure of the investigated signal. For a healthy subject, the values of the Hurst exponents vary from 1.2 to 0.75 for different values of the q parameter. This is evidence that the healthy subject has multifractal behavior. In a patient with arrhythmia, the values of the Hurst exponents are almost constant, evidencing monofractal behavior. The values of the multifractal spectrum of the studied groups (Table 7) is statistically significant, therefore the two groups can be distinguished by the MFDFA. * The R/S plot is one of the most popular nonlinear methods for time series analysis that is based on Hurst exponent calculation. The fractal behavior of the signals is characterized by Hurst exponent values in the range (0.5, 1.0). The Hurst exponent values of the arrhythmia patient group are about 0.55, while the subjects of the healthy group have higher values (Table 7). The higher Hurst exponent value is evidence of a higher PRV and better health. * The AppEn and SampleEn values that are shown in Table 7 are statistically significant, therefore these two parameters can be used to distinguish healthy controls from patients with the disease. AppEn and SampleEn values are higher in healthy controls, which is evidence that their PP data have stochastic, fractal behavior.