Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 68, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2021.102766
Keywords
Biometrics; Electrocardiogram; Functional dependencies; Structural dependencies; Genetic programming; Convolutional neural networks
Categories
Ask authors/readers for more resources
Biometric recognition systems can utilize physiological signals like electrocardiogram (ECG) for identification, and a personal biometric recognition system has been designed to estimate these medical variables.
Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and crosscorrelation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available