QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

Abstract

Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively.