# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 2 Related Work  
Due to the general interest in medical research, there exists several public ECG databases, available in Physionet [9]. Some of the most important ones were developed before ECG was used in biometrics. However, they have been relevant at the early stages of this modality. The most popular ones are the MIT-BIH Normal Sinus Rhythm (MIT-BIH NSR) and PTB [10]. However, as the main focus was related to healthcare, the acquisition does not consider concerning issues in human recognition. Some issues that affect the field are related to variation between days, data quality, sensor placement, complex and expensive sensors, or being capable of performing under different environmental conditions. Specifically, the ECG can be affected by the user's physical activity before or during the acquisition process, as the heart rate fluctuates, stretching or widening the signal. The ECG-ID database [11] is the only public database collected with an on-the-person sensor that considered some of these issues. The ECG-ID database was formed by shorter signals with different physical positions for the subject: sitting and free movement. These features made a more realistic biometric environment, as it required shorter acquisition and included the possibility of physical movement. Since year 2005 more databases have been acquired for research in biometrics and on-the-person sensors, although they remain private [12]. Databases such as those in References [13,14] collected information from 502 and 269 users  
Figure 1: Normal Sinus Rhythm waveform and some of its relevant points. respectively, starting with 10 s resting signals. The great number of users, as well as the type acquisition, situated these data closer to realistic biometric environments. However, long-term variations were not considered as those in the clinical database in Reference [15], which was formed by 10 s sessions of 460 patients, where the verification phase was carried out with at least one year difference. The measurement of the heart activity presents several noise sources such as the baseline wander (0.2-0.5 Hz), power-line interferences (50-60 Hz), and muscle artifacts related to muscle movement (around 100 Hz). Discrete Cosine Transforms (DCT) [16] and Convolutional Neural Networks (CNN) [17] are two of the approaches to reduce the mentioned noise. However, the most frequent in literature is applying band-pass filters [18]. Feature extraction is usually applied after signal conditioning, where approaches are classified in two clear strategies: fiducial and nonfiducial features. Fiducial features are based on measuring different parts of the signal such as the amplitude of the R-peak of the width of the QRS complex, requiring a precise detection of these points, which is complicated to achieve. On the other hand, nonfiducial features are based on measurements or transformations that are only applied directly to the signal, without identifying specific points. After preparing the data, training the classification model is the next step. Support Vector Machines (SVMs) and k-Nearest Neighbour (k-NN) algorithms are frequently applied in this stage for ECG biometrics [10] and other similar fields related to pattern recognition. However, the tendency is shifting to Neural Networks (NN), as they generalize properly from raw data, requiring less complex preprocessing techniques and achieving better results [19]. Thus, NN helped in simplifying the fiducial point detection to the point where, in some cases, it becomes expendable. Common machine learning techniques such as Multilayer Perceptron (MLP) have been applied to classification, but the higher complexity of Deep Neural Networks (DNN) is currently a common approach at this stage. In Deep Learning, CNNs are commonly applied in research related to images. Some works use them as the main feature extraction tool in tasks, such as handwritten digit recognition [20]. However, they are also applied to problems related to 1D signals such as voice, where they contribute to a fast feature learning [21].