## 3 Results
### Results for Different Enrolment Sizes  
The determination of the most optimal MLP configuration was done using only one of the visits. It was required to split the data between training and validation, using only half of the available data for modeling. Using the highest amount of possible data could lead to better results. However, providing too much information could cause overfitting, resulting in a model that is not capable  
\begin{table}
\begin{tabular}{c c} \hline Number of hidden layers & 1 \\ \hline Hidden layer size & 200 \\ \hline Activation & Identity \\ \hline Alpha & 0.001 \\ \hline Tolerance & 0.05 \\ \hline [PERSON] & Adam \\ \hline Learning rate step & 0.0001 \\ \hline No change iterations & 10 \\ \hline \end{tabular}
\end{table}
Table 8: Final MLP configuration.  
Figure 10: Results for every test set using (**a**) ND, (**b**) FD, and (**c**) SD signals with 50% of Ex as training.  
of generalizing properly with new samples. The previous section already set the most suitable configuration when using 50% of R1 data with FD. The goal of this experiment is to vary the training proportion to observe how the initial results get affected. The training is done using 25%, 40%, 50%, 75%, and 100% of the available R1 FD data, while the testing is the same as the one in the results between different visits. This data is plot in Figure 11. The different performances under S are plot in Figure 11. The lowest EER is obtained when using 100% of the available data in training, increasing as the proportion decreases. However, we can see that there are not huge differences between using 60% or 75% of the data, as it also happens with 40% and 50%. However, when data is reduced to only 25%, the EER almost doubles the previous result. Similarly, Figure 11 represents these results in the case of R2. In this visit, using 100% provides closer results to those in 80 and 75%. As seen in the case of S, 40% and 50% do not provide significant differences, and decreasing the enrol data to 25% provides the same impact as before. In the case of Ex, represented in Figure 11, results do not change significantly between sizes. Even though we observe 100% as the higher in EER, given that all results are close to each other, this subtle difference could have nothing to do with the training size.  
All the EER results are collected in Table 9. As seen graphically, using only 40% of the available data for enrolment provides similar results to those in 50%, being even slightly improved. This change could mean going from 125 necessary cycles to 100, decreasing the enrolment data acquisition in a few seconds. Increasing to 60% also makes a slight improvement, although not in the case of Ex. Using 75%  
Figure 11: Performance results with different sizes of enrolment for (**a**) S, (**b**) R2, and (**c**) Ex data as test set with the final MLP configuration.  
of the data results in the lowest EER for Ex and one of the lowest for S and R2, being under those obtained in 100%. However, the different EER results when increasing the proportion from 75% to 100% is noticeable, as S and R2 perform better by causing a poorer performance in Ex. This is one of the reasons to avoid using such a long enrolment if there are chances of having very different heart rates in recognition.  
After analysing these results, we can say that our system can reach results between 2.79% to 4.95% EER, using 100 QRS complexes. If the purpose of the system requires higher performances with the expense of longer enrolments, increasing the size of the data set is a good approach. Providing 187 QRS complexes for enrolment provides an EER between 2.69% and 4.71%. Finally, results in terms of accuracy are represented in Table 10 applying a threshold of 0.5. The achieved accuracy under every condition is higher than 97% considering single heartbeats. We can also observe that the accuracy increases slightly up to 1% when increasing the training data.