## 3 Results  
Before weighing the yields achieved and comparing them with other approaches published in the scientific literature, it is worth examining the entire process that has led to the CNN model presented in this paper.  
First, as an example of preprocessing data, Figure 4 shows two input \((p,q)\)-planes, one from the basal state, as shown in Figure 4a, and the other from the stress state on the same subject, as illustrated in Figure 4b.  
Considering the \((p,q)\)-planes, a more regular spiral configuration can be seen in the case of the user in the basal state, mainly in the central bulb, as shown in Figure 4a. In a stressed state, the geometric structure experiences a greater spatial dispersion both in the arms of the spiral and in the central bulb, which adopts a more ellipsoidal arrangement, blurring the regular configuration, as reflected in Figure 4b. In any case, in the absence of a more exhaustive study of the geometric structures exhibited by the \((p,q)\)-planes and their functional link with the physiological system that generates the PPG signal, we believe that the mechanism behind the blurring of the configuration regular in a kind of multifractal tessellation is related to a dynamic tending towards chaos.  
In more physiological terms, it leads to a more flexible and adaptable physiological disposition to quickly counteract the stress response and restore the homeostatic balance of the organism as soon as possible. In this regard, applying a convolutional network model has been pivotal in detecting an individual's basal or stress state. The successive  
Figure 4: (Color online) Example of preprocessing data (CNN input data) with PPG signals shown in Figure 2. (**a**) Subject #1 in the basal state; (**b**) Subject #1 in the stress state when confronted with a videotaped memory task.  
convolutional layers of the network learn a hierarchy of invariant features in the \((p,q)\)-planes, unique to each individual and indiscernible to the human eye. An in-depth analysis of the maps obtained by the different layers of the model's feature extractor would provide relevant information on these factors common to all individuals to further advance the study and classification of stress at its different levels.  
Next, let us begin by evaluating the training and validation process illustrated in Figure 5a,b, respectively, for the 100 iterations (_epochs_) established by design. As can be seen, the training and validation processes behave similarly. In the first few iterations, as Figure 5a shows, until around iteration 16, you do not notice an increase in precision, as you would ideally expect. During the first iterations, the precision fluctuates and then quickly scales up to a value close to 100% (\(\sim\)97%), remaining around 97% from iteration 40 onwards. The loss function, as illustrated in Figure 5b, remains virtually stable around 0.7 because, not without some logic, learning is hard and slow in the first bars. Subsequently, the loss function decreases sharply until iteration 40, slowly decreasing until it reaches a value close to 0 (\(\sim\)0.13). Optimizing the gradient descent algorithm keeps significant parallelism between the training and validation data. This conformance in the training and validation data behavior is due to the Dropout layer introduced at the end of the convolutional network. The Dropout layer avoids the slight overfitting that comes with a somewhat more complex model than is strictly necessary.  
The trained model, after overfitting is removed, consists of four convolutional layers with _kernel_ of 13 \(\times\) 13, 13 \(\times\) 13, 9 \(\times\) 9 and 9 \(\times\) 9, respectively and associated Max Pooling layer of 2 \(\times\) 2 (for the four layers), Flatten layer and Dense layer of 12 neurons (_batch size_ of 30 and number of _epochs_ equals 100) and the optimizer _Adam_.