## 3 Results
### Results Comparison  
There are heterogeneous techniques throughout literature along with all different steps in the process. Not only results but also tools need to be taken into consideration. The selected works to compare our results to, are chosen due to similarities with the present work, as well as being relatively recent, going from 2017 to 2019. The techniques and some of their results are summarized in Table 11.  
The criteria to select these works were based on several facts: getting databases whose characteristics were the closest to the proposed one, while having similar segmentation processes and/or same classification algorithm. However, fulfilling all conditions at the same time of providing similar procedures in testing and metric calculations is very complicated. We have found that similar works usually implement the classification accuracy as performance measurement, instead of calculating DET graphs. In addition, due to different procedures and tools, having higher accuracies or lower EERs does not imply better performances, as conditions vary among all of them. There are even discrepancies when working with the proposed database, as the verification performance is obtained considering its extended version.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enrol Proportion** & **Visit** & **S** & **R2** & **Ex** \\ \hline
25\% & 97.48 & 97.55 & 97.48 \\ \hline
40\% & 97.82 & 97.89 & 97.78 \\ \hline
50\% & 97.98 & 98.05 & 97.78 \\ \hline
60\% & 98.11 & 98.17 & 98.04 \\ \hline
75\% & 98.26 & 98.35 & 98.15 \\ \hline
100\% & 98.40 & 98.49 & 98.26 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy (%) for different enrolment proportions when using R1 FD for enrolment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enrol Proportion** & **Visit** & **S** & **R2** & **Ex** \\ \hline
25\% & 5.42 & 3.64 & 5.05 \\ \hline
40\% & 3.64 & 2.79 & 4.95 \\ \hline
50\% & 3.64 & 3.00 & 5.45 \\ \hline
60\% & 2.73 & 2.46 & 5.42 \\ \hline
75\% & 2.69 & 2.09 & 4.71 \\ \hline
100\% & 2.02 & 1.88 & 5.52 \\ \hline \hline \end{tabular}
\end{table}
Table 9: EER (%) for different enrolment proportions when using R1 FD for enrolment.  
These drawbacks are also the reason to not test the proposed algorithm in public databases. This work deals with the problem of verification using ECG with significant heart rate variations, but there are no public databases with these characteristics. As most databases are focused on pathology detection, they include non-healthy subjects, colliding with the assumption of healthy sinus rhythm for the QRS segmentation. Moreover, the databases that collect information on healthy users do not provide heart rate changes to test our approach.  
Nonetheless, our work provides good results in comparison to the work with the extended version of the database [10]. We need to emphasize that in the extended version, 49 out of 104 subjects provide the Day 1 scenario in both days, making the EERs not comparable. Obtained accuracies for the best solutions (40% and 75% of training data) are also good reaching up to 98.35%. The proposed classifier structure is a very simple ANN that only requires using differentiation in the segmented QRS, avoiding more complex transformations, and decreasing the computational cost, which is important to export algorithms to mobile devices. It also deals with a good number of users and considers different more physiological conditions that are not considered in the ECG-ID database.