## Materials and Methods
### Algorithm Modelling
#### 2.2.1 Database  
As ECG signals provide sensible information, releasing databases to the public is complicated due to privacy policies, leaving only a few databases to work with. Several databases are public in Physionet [33] being MIT-BIH Normal Sinus Rhythm (MIT-BIH NSR) and PTB [34] the most common ones in the literature. However, none of them are thought for human recognition but for helping in pathology classification, thus the acquisitions are done with commercial electrocardiographs. These databases provide long signals that are minutes or even hours long, while having the user in limited physiological conditions, usually resting. The ECG-ID database [35] is the only public one that aims for human recognition, providing 20-s signals. They are acquired by wrist electrodes and under two different conditions: sitting and free movement, making ECG-ID the closest to a biometric scenario.  
According to ISO 19795 [36], data collection needs to be representative of the target application. This is the reason why in this case we need to consider that heart rates fluctuate constantly under different situations like stress and exercise. This parameter can be controlled in enrolment by helping the subject to relax. However, that is not something doable in recognition, where the subject must be independent. Adding this extra step would make the system less user-friendly and inconvenient. With these considerations, the previous databases focused on medical purposes do not provide the required data for biometrics. These databases tend to have long acquisitions and/or do not usually provide visits under different scenarios and/or days. In the case of ECG-ID, the free movement scenario is not specific enough about the activity, so the heart rate increase is not pursued, not making it useful for the study under more specific physiological conditions.  
In terms of the capture device, working with professional medical sensors provides higher signal quality. This approach is chosen to remove as much noise as possible, maximizing the isolation of the signal behavior, even if it does not have the required usability for biometrics. The database acquisition is done using a Biopac MP150 system with a 1000 Hz sample frequency, as described in Reference [10]. The sensors obtain Type I lead, which is convenient in biometrics because it only involves the arms,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Identity** & **Logistic** & **Tanh** & **ReLU** \\ \hline
**Formula** & \(g(x)=x\) & \(g(x)=\frac{1}{1+e^{-x}}\) & \(g(x)=tanhx\) & \(g(x)=max(0,x)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Most common activation functions.  
measuring the voltage difference between left and right. Moreover, the placement of the limb sensors requires little expertise so their placement is similar between the different visits, in the pursuit of reducing this type of noise. The present work uses the second part of the extended database, as it is the only one that provides different physiological states. The collection is done with 55 healthy subjects in 2 different days, with two visits per day distributed as shown in Table 2. Each visit records 5 signals of 70 s duration per signal with a 15 s posture adjustment between every recoding.  
The applied database remains private, due to the General Data Protection Regulation (GDPR) by the European Government. The law started its implementation in 2018 and considers that biometric data is sensitive. The GDPR takes into consideration the potential need to use this data in research but demanding some specific conditions. As this database was collected before the GDPR implementation, it does not fulfill the legal criteria to get published.