# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
###### Abstract  
Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.  
ECG biometrics; Deep Learning; pattern recognition; human verification; Convolutional Neural Networks; Long-Short Term Memory +
Footnote †: 2021: 11, 5880. [[https://doi.org/10.3390/app11135880](https://doi.org/10.3390/app11135880)]([https://doi.org/10.3390/app11135880](https://doi.org/10.3390/app11135880))  
Despite their generalities, these signals still present interindividual variability [5], making individual identification possible. In addition, it has been proven that the long-term intraindividual variability is comparable to day-to-day variability [6], allowing comparisons in different periods, even considering verifications with more than one-year separation [7]. Moreover, ECGs can be obtained from every live being, providing life-proof detection. These signals are difficult to fake, as they represent the functioning of an organ, which is involuntary and its activity cannot be accessed easily. These characteristics gift ECGs with great potential for biometrics and have been studied in this field since the early 2000s [8].  
The present work deals with data acquired under different conditions related to time, position, and heart rate, as the process took place on two different days, with sitting and standing positions and after increasing the heartbeat frequency with exercise. The classification was achieved by using a combination of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM). This choice helped in using the minimum data preprocessing and delivered information on how these techniques generalize when employing more realistic signals. The influence of factors such as the type of enrollment (one or two days) or length were observed and discussed through the hyperparameter optimization. A final configuration was set, providing general improvements, especially in those conditions related to unstable signals.