# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 5 Discussion  
Current approaches in ECG for human recognition are based on Deep Learning. These tools require representative data in order to perform at their maximum capacity. However, the selection of published ECG databases is limited, and most of them are not representative for a biometric scenario. The present work dealt with ECG data acquired in different positions, heartbeat frequency, and days in an attempt to further study its potential as a biometric signal. The results were achieved by applying a neural network as a classifier, BioECG, as it is a successful approach through literature. Through the tuning process, the influence of heart rate, position change, and long-term variations are also discussed. The achieved tuning procedure was extensively done using different numbers of samples to observe its influence. However, results related to the number of samples in the enrollment were heterogeneous depending on the evaluated experiment. Choosing the final model to be taken requires knowledge about the system's purpose. A final configuration was specified as a compromise solution considering results in different scenarios and enrollment size. Based on that final tuning, this work has focused on one-day and two-days \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dataset** & **Enrollment** & **Features** & **Classifier** & **EER (\%)** \\ \hline \multirow{2}{*}{Proposed work} & \multirow{2}{*}{S2} & One-day & \multirow{2}{*}{QRS complex} & \multirow{2}{*}{BioECG} & 0–10.13 \\  & & Two-days & & & 0.03–3.93 \\ \hline
[36] & S2 & One-day & First differentiation QRS complex & MLP & 2.69–4.71 \\ \hline \multirow{2}{*}{Proposed work} & \multirow{2}{*}{S1 + S2} & One-day & \multirow{2}{*}{QRS complex} & \multirow{2}{*}{BioECG} & 0–5.31 \\  & & Two-days & & & 0–1.35 \\ \hline
[26] & S1 + S2 & One-day & \multirow{2}{*}{Hearbeat SWT + IFS} & LDA & 1.74–5.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison with similar works that use the proposed database. Results are represented in a range of EER, where the first value is the result for verification with data in the same conditions as the enrollment. The range upper bound is the result for verification using data after exercise. enrollment processes while using the same number of total samples. The different scenarios proposed in the database gave information about how different day data help generalize unstable signals, too. One-day enrollments provided good results when dealing with data acquired in the same conditions, such as day and physical scenario. EERs increased noticeably when calculated with data in different days, even considering the same resting state. Adding an extra day of enrollment affected verification in the same day, as a consequence of sharing the available data with a enrollment from another day. However, the error increase was low, even more if it is in exchange of the improvement under more complex conditions. As a consequence, results for visits in the second day were highly decreased up to 99% when dealing with resting scenarios. For high heart rate conditions, a second day of enrollment improved the EER from 64% to 91% with respect to one-day enrollment. This observation demonstrated how obtaining data in different days helped generalize more unstable signals. Results for one day of enrollment were comparable to a previous work that dealt with the same database, with the difference that the present work required less data transformation in preprocessing. In the case of managing data in resting state, the results were improved even for enrollments in one day. After observing the results in BioECG with low signal processing, adding extra steps in this stage could provide more insight into this modality. More complexity in the R-peak detection including Deep Learning could help select better data. As the database was limited to two acquisition days, adding extra visits for different days should be considered.