# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 4 Experimental Analysis
### Final Configuration  
Considering the different results in the previous section, the solution to the final system's configuration was not unique. Depending on its purpose, some factors needed to be taken into account. The enrollment process needed to be long enough to provide good information for the development set. However, if the enrollments were too long (i.e., a greater value of \(d\)), the user might get tired. Adding an extra day of acquisition has been proven to provide better results. Unfortunately, users are usually reticent to extend the enrollment process to several sessions. However, if the system was required to have higher performance, it may be worth the effort. If the purpose of the system focused on fast recognition more than high performances, the enrollment process could get shorter and easier. It also would depend on the probability that users come up with different position or heart rate throughout recognition, e.g., members going in the gym may have different heartbeat frequency as when they go out, as they may have not been fully recovered yet. Once these issues have been addressed, this work suggests one specific configuration as a trade-off choice, which was considered to provide good general verification results in all different scenarios. Doing a two-days enrollment is key for increasing the verification performance and even more if there are heart rate variations in the recognition process. The chosen enrollment size is \(d=0.5\) as it was a frequent value when obtaining the best discussed results. However, when it was not the best of all the proportions, it still performed properly while allowing to have a shorter enrollment process. Looking at Table 3, the enrollment required 125 samples per user between two-days. That means around 63 QRS samples per visit, which requires two ECG signal acquisition as 50 complexes are extracted from each one. It summarized in an enrollment process of a maximum duration of 140 s, as every 50 peaks requires 70 s of acquisition. Setting \(N_{p}\) to a higher number would allow for the enrollment of people with one signal acquisition, depending on the user's resting heart rate. The hyperparameter tuning provided as the best configuration \(b=20\), \(L=2\), and \(n=64\). The tuning reached 151 epochs following the early stopping criteria. The mean time taken for training each fold in cross-validation was 147.7 s for the whole S2 dataset. The system was trained according to these hyperparameters for S1 and S2 independently. The same process was achieved using S1 and S2 as a whole dataset too, providing heterogeneous samples as D1V2 and D2V2 are different scenarios. Verification performance results under these conditions are collected in Table 8. Using the S1 and S2 jointly in training allowed one to obtain the DETs in Figure 7, where Figure 7a,b represent results for one-day and two-days training, respectively. Analogously, Figures 8 and 9 are the DETs for S1 and S2 respectively, where Figures 8a and 9a are DETs for one-day data training and Figures 8a and 9b for two-days in training. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Visit**} & \multicolumn{2}{c}{**D1V1**} & \multicolumn{2}{c}{**D1V2**} & \multicolumn{2}{c}{**D2V1**} & \multicolumn{2}{c}{**D2V2**} \\ \hline
**Set** & **Train** & **One-Day** & **Two-Days** & **One-Day** & **Two-Days** & **One-Day** & **Two-Days** & **One-Day** & **Two-Days** \\ \hline
**S1 + S2** & 0 & 0 & 1.90 & 0.70 (\(-63\)) & 4.72 & 0.01 (\(-99\)) & 5.31 & 1.35 (\(-74\)) \\
**S1** & 0 & 0.45 & 0 & 0.57 & 2.30 & 0.24 (\(-89\)) & 2.70 & 0.24 (\(-91\)) \\
**S2** & 0 & 0.03 & 5.27 & 3.93 (\(-25\)) & 5.43 & 0.17 (\(-95\)) & 10.13 & 3.64 (\(-64\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: EER (%) results for the final selected hyperparameters, with the different datasets (S1, S2, and S1 + S2) with one-day and two-days data in training. The EER variation between one-day and two-days is detailed as a percentage (%) in parenthesis when both data are non-zero.