## 5 Conclusions  
In this article, we propose a binary classification model based on CNNs to detect the presence of acute stress in a subject through the PPG signal. Unlike other previous works, our model only requires a single characteristic of the PPG signal, its diffusive dynamics, a property inherent to the vascular bed of each human being, unrelated to external conditions and very stable over time except in the case of pathologies that could damage the vascular structure. Most works that use biological signals to identify stress episodes implicitly or explicitly resort to temporal or frequency characteristics subject to their morphology. Therefore, they are very vulnerable to noise in data-acquisition systems and eventual psychophysiological variations, such as physical activity or a change in an emotional state, which distort an accurate measure of stress. The PPG signal's diffusive dynamics reflect each individual's reactive and inalienable tendencies, less prone to exogenous and endogenous spurious disturbances that undermine the veracity of the stress diagnosis. The solvency of the diffusive dynamics of the PPG signal in the face of external and internal instrumental artifacts makes it possible, with its single analysis, to identify episodes of acute stress with a high percentage of success with a minimum signal sample. The solution proposed in this work reaches 97% accuracy, like its \(F_{1}\)-score, with a sensitivity of 99%. With a latency time of at most 20 ms, the model requires only 4 s of PPG signal to report a stressed state. In addition, the modest memory requirements, \(\sim\)2.8 MB, make our solution a highly attractive alternative for implementation in consumer electronics (portable devices), which would allow not only early and accurate stress detection but also the expeditious deployment of the necessary countermeasures before their adverse effects have a significant impact on the family, economic and work spheres. The CNN model has obtained a very positive evaluation in terms of the different evaluation metrics that commonly certify the validity of a model. A weak point that partly undermines its respectable credit is the FP value, 50 of 780, compared to the FN value, 3 of 780. Although the FP value is not entirely unacceptable, with a lower cost than the impact of FN, it is worth further work to refine the model, mitigate the losses subject to the depth of the CNN and minimize their degree of incidence. Along the same lines, future work should delve into the physiological mechanisms of stress and how they converge in the diffusive dynamics of the PPG signal so that the CNN model can be optimized with an efficient and systematic adjustment of the hyperparameters. Its diagnostic prospects are promising, even as a recommender system. However, its expectations will be fulfilled when it helps to detect stress sufficiently in advance, partly mitigating the serious consequences that it derives from modern society. **Author Contributions:** Conceptualization, J.d.P.-C., J.C. and D.F.-J. ; methodology, J.d.P.-C., J.C. and D.F.-J. ; software, J.C. and D.F.-J. ; validation, J.d.P.-C., J.C. and D.F.-J. ; formal analysis, J.d.P.-C., J.C. and D.F.-J. ; data curation, J.d.P.-C. and A.P.G.-M.; investing, J.d.P.-C., J.C. and A.P.G.-M.; resources, J.C., A.P.G.-M. and M.F.C.-U. ; writing, original draft preparation, J.d.P.-C., J.C. and D.F.-J. ; writing, review and editing, J.d.P.-C., J.C., D.F.-J. and A.P.G.-M.; visualization, J.d.P.-C. and J.C.; supervision, M.F.C.-U. and A.P.G.-M.; project administration, M.F.C.-U. and A.P.G.-M.; funding acquisition, M.F.C.-U. and A.P.G.-M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used in this study come from the FIS-PI12/00514 project in the Universidad Politecnica de Madrid.