A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN. (July 2021)

• Record Type: Journal Article
• Authors: Sepahvand, Majid; Abdali-Mohammadi, Fardin
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Biometrics -- Electrocardiogram -- Functional dependencies -- Structural dependencies -- Genetic programming -- Convolutional neural networks; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102766
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: This paper proposes a method for the biometric recognition of a multilead ECG signal. Functional and structural dependencies were calculated as medical variables. Within and cross correlation were determined to estimate functional dependencies. GP-based evolutionary CNN trees were employed to estimate structural dependencies. CNN trees perform the deep feature learning process through morphology operators. Abstract: 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 cross-correlation 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.