## 4 Discussion
In that sense, it is worth highlighting the work of [PERSON] et al. [67], in which the authors propose a debugging method (_denoising_) of the signals PPG data in order to refine its temporal definition and thus to be able to extract higher quality features. The authors demonstrate how removing noise from the PPG signal achieves stress detection with a higher accuracy rate than other conventional approaches. In the same way, in the work of [PERSON] et al. [68], the first _hardware_ solution is proposed that enables real-time detection of stress. However, to achieve high effectiveness, due to the susceptibility of the PPG signal morphology, the use of 60 signal characteristics is prescribed. With these backgrounds, the solution proposed in this work is entirely new; it ultimately moves away from the usual methodology, anchored in the morphology of the PPG signal and approaches the detection of stress from the dynamic approach of the PPG signal, inspired by the stochastic nature of blood flow. A diffusive model highly dependent on the vascular bed, whose physical structure is unique to each individual, is a framework to characterize an individual's stress level through a CNN. According to Table 4, it can be seen how the initial attempts for stress detection assume a multimodal character in the extraction of physiological characteristics. For a more comprehensive review, we recommend consulting the works of [PERSON] and [PERSON] [59] and [PERSON] et al. [65], apart from those specifically referred to in Table 4 and the references within them. The conjunction of various biological signals in the stress-detection process, added to the computational requirement inherent in the complexity of the feature-extraction algorithms, make it impossible to integrate a possible _hardware_ solution that operates in real-time in a portable device [69]. With this objective, in recent years, stress-detection models have been proposed that reduce the analysis to a single biological signal from which to extract reliable stress indicators. In addition, the advanced signal-processing techniques that accompany the most modern classifiers favor optimal levels of discrimination between psychophysiological states. As is known, stress is closely linked to the cardiorespiratory system, whose physiological manifestation extends in its entire spectrum throughout the entire organism, including, e.g., breathing, autonomic nervous system, skin temperature and sweating. The ECG signal is its maximum exponent; therefore, it has been used in countless works, leading to the detection of stress [59]. However, its acquisition is uncomfortable for the subject on which the electrodes required for its measurement are arranged. In this sense, the PPG signal has become a reliable alternative to the ECG signal since it is effortless to acquire, with minimally invasive techniques and it contains the same physiological information as the ECG signal [84]. Focusing on those previous works that only use the PPG signal to detect the presence of stress in a subject, with an accuracy greater than 90%, [PERSON] et al. [72] use seven extracted characteristics of the PPG signal. The pulse signal was acquired using a web camera from the facial analysis of the face. They achieve 94.4% accuracy using an SVM BRF classifier. [PERSON] and [PERSON] [77] use 18 characteristics of the PPG signal to identify a possible stress episode, 9 of them from the time domain and the remaining 9 from the frequency domain. With a DNN, they reach 91% accuracy. [PERSON] et al. [67] propose a stress-detection methodology based on the analysis of 26 characteristics of the PPG signal. With different classifiers, they obtain a maximum accuracy of 96.5% whenever an LDA classifier is used. In this work, unlike the previous ones, the authors demand a minimum trace of the PPG signal, in their case of 120 s, to guarantee the published performances. [PERSON] et al.