## 4 Discussion  
This work proposes a machine learning algorithm for ECG verification by avoiding extra calculations in the feature extraction process. This is achieved by re-using already calculated data in the segmentation process to train the model. Skipping these extra calculations reduces the complexity and time consumed by the system while using simple differentiated QRS complexes. The selected classification algorithm is the MLP neural network, which gets its optimal hyperparameter configuration after a tuning process. To reach this goal, we applied a database which considers specific physical states and different acquisition days to obtain more information about the recognition. The tuning process and the following performance evaluation have provided a deep study in the behaviour of the different types of scenarios. In addition, the optimal configuration provided an interesting result--the final activation function is identity, which corresponds to a linear function. This feature leads the system to be simplified to a linear system, which implies the possibility of being solved as linear regression. Even though more activations such as ReLU also achieved good results in tuning, finding out the results of this approach could lead to relevant information. Doing further research in this matter can provide information about how these data behave and if the selected features are representative enough, simplifying the classification. Regarding the final MLP model, we have observed that the first differentiation in the QRS complex provides the best results. From these results, we can infer that how fast the waveform varies is a way to enhance the differences among users. In addition, we have seen how using different sizes of enrolment data affects the performance. The selection of this feature depends on the purposes of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Database** & **Users** & **Features** & **Classifier** & **Result** \\ \hline
[15] & Private & 18 & Heartbeats & MLP & Accuracy: 89\% \\ \hline
[31] & ECG-ID & 90 & Heartbeats & MLP & Accuracy: 84–89.3\% \\  & & & & LDA & Accuracy: 93.28\% \\ \hline
[10] & Proposed database (extended) & 104 & \begin{tabular}{c} SWT \\ for heartbeats \\ \end{tabular} & LDA & EER: 1.74–5.47\% \\ \hline Proposed work & \
\begin{tabular}{c} Proposed database \\ (only users with exercise) \\ \end{tabular} & 55 & QRS complex & MLP &
\begin{tabular}{c} Short enrol: \\ EER: 2.79–4.95\% \\ Accuracy: 97.78–97.89\% \\ Long enrol: \\ EER: 2.69–4.71\% \\ Accuracy: 98.15–98.35\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of results and tools from state-of-the-art with similar characteristics. the biometric system, as some environments require shorter enrolment processes but do not mind lower performances, while others could choose otherwise. In conclusion, we have shown that using around 100 QRS complexes is good enough for the system, and it is not recommended to exceed 190 in enrolment unless it is known that the recognition data is controlled and heart rates are low enough. These configurations reach the best EER of 2.69% to 4.71% when applying the longest type of enrolment. Nonetheless, looking for more combinations of the signal differentiation could be a new way to improve performances. The present work also focuses on the differences between three types of physiological situations for the user, due to a lack of public databases with these characteristics. The chosen database collects different visits in different scenarios, proving that MLP is capable of working properly under these conditions. The system also reduces the computational complexity and time with its simplicity. This matter is key for potential algorithm adaptation in mobile devices, setting a good precedent for further research on this issue. Moreover, we have proven that as it can be expected, different heart rates between the enrolment and recognition data provide lower performance.