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.

BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

“…Works related to the applied database were limited due to its privacy. However, a similar analysis was provided in a previous work [36], where subset S2 with MLP classifiers was employed. The enrollment was longer, with 187 QRS complexes for the best results, and it required the first differentiation of the QRS.…”

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

“…Most of the studies only consider supine rest conditions, which represent an important limitation regarding the use of ECG-based biometric systems in real-life contexts. Tirado-Martin et al [ 21 ] acquired signals in different posture positions: sitting down at rest, standing at rest, and after exercise. They proved that different heart rates between the enrollment and recognition data result in lower performances.…”

“…Used in conjunction with AcqKnowledge software and BIOPAC electrodes, amplifiers, transducers, and other system components, the MP160 is part of a complete data acquisition and analysis system. Many researchers used the BIOPAC system for data acquisition of their proposed biometric system [ 21 , 22 , 23 ].…”

Detection of multi-class arrhythmia using heuristic and deep neural network on edge device

“…Automatic arrhythmia detection has been studied by several researchers [3]- [12]. They used a heuristic [3]- [9] and machine learning [10]- [12] method to search for QRS waves.…”

“…Automatic arrhythmia detection has been studied by several researchers [3]- [12]. They used a heuristic [3]- [9] and machine learning [10]- [12] method to search for QRS waves. The algorithm proposed by Pan and Tompkins [3] is studied QRS complex detection based on a microprocessor with detection steps: noise removal using a bandpass filter, Derivative signal ECG, squaring signal, moving window integration, thresholding to find the region of interest (ROI) of QRS Complex.…”