Machine Learning
Pattern Recognition Letters
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
Robust ECG Biometrics by Fusing Temporal and Cepstral Information
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Eulerian video magnification for revealing subtle changes in the world
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation
Computer Methods and Programs in Biomedicine
QRS detection-free electrocardiogram biometrics in the reconstructed phase space
Pattern Recognition Letters
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (100Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30Hz and 60Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30Hz and 60Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360Hz (the maximum frequency existing in our database). We also evaluate the impact of: (1) the number of training and testing samples for learning and identification, respectively; (2) the scalability of the biometry (i.e., increment on the number of subjects); and (3) the use of multiple samples for person identification.