## 1 Introduction  
In the last years, improvements in sensors have produced more affordable and faster biometric acquisition systems, with higher processing capabilities. Besides, evolution in mathematics has led to faster and more accurate pattern recognition algorithms, such as those covered in machine learning.  
Electrocardiogram (ECG) biometrics is one of the biometric modalities that has been noticeably improved thanks to machine learning algorithms. This modality is based on the electrical activity in the heart related to time represented by ECGs. ECGs are measured with electrocardiographs through different parts and angles of the body, allowing them to obtain the signal from several perspectives. Its standard acquisition results in 12 different types of signals or leads that provide different information about the heart's performance [1]. These signals get frequently classified in two types [2]:* Limb leads: require four sensors, placed on the right arm, left arm, left leg, and right leg. The latter functions as a ground. Limb leads are also divided into two categories:
* Standard bipolar leads (I, II, and III): measure the voltages between left arm-right arm, left leg-right arm, and left arm-left leg, respectively.
* Augmented unipolar leads (aVR, aVL, and aVF): measure the relative voltages concerning the extremities, instead of using ground references. They allow us to observe the previous signals from different angles.
* Chest (precordial) leads (V1, V2, V3, V4, V5, and V6): are calculated considering ground as a reference and require of six sensors placed carefully on different parts of the chest.  
Figure 1 represents the general sinus rhythm signal, formed by the P waveform followed by the QRS complex and the T waveform. The different waveforms provide information about how the different areas of the heart perform. P represents the depolarization of the atria. The QRS complex is a consequence of the depolarization of the ventricles, and the T wave shows their repolarization [3].  
ECGs can be used as a biometric signal because they are universal, unique, invariant enough with respect to its template and quantitative [5]. Every alive person can provide an ECG and it can be measured with commercial electrocardiographs. This biological signal also depends heavily on the morphology of the heart, which is unique even among healthy individuals. The addition of variations due to physiological conditions related to skin properties, gender, or age, makes ECGs unique for every person [6]. They are also considered stable in the long term and provide good data separation between genuine users and imposters [7]. To our knowledge, the first approaches using ECG signals for human identification were released in 2001 [8; 9].  
These types of signals are interesting for biometrics because of their nature. They are difficult to access without the user's cooperation and susceptible to the specific scenario the user is in, due to variations such as those caused by heart rate o amplitude changes. These characteristics make them difficult to counterfeit while providing them with a lot of potential in biometrics.  
The present work provides a study of the potential of Multilayer Perceptron (MLP) Neural Network structure for ECG biometric verification, extending the achieved results in Reference [10]. Achieving good performances with a classifier such as MLP results in relevant information, due to the challenging properties of the applied database. In addition, the feature extraction process is  
Figure 1: Normal Sinus Rhythm waveform and some of its relevant points [4].  
simplified by re-using the calculated data in segmentation, reducing it to a simple window selection. This simplification allows us to reduce computational processing time and cost. Finally, we provide the optimal configuration for the classifier, including the type of features and data size, as well as a discussion about the information these results provide about the dataset.