Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 22; p. 9179
• Main Authors: Zhang, Xu, Liu, Qifeng, He, Dong, Suo, Hui, Zhao, Chun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Accuracy; Algorithms; biometric; Biometric identification; Biometrics; Biometry; Data compression; Decomposition; dictionary learning; Electrocardiogram; electrocardiogram (ECG); Electrocardiography; Methods; Noise; Signal processing; sparse coding; wavelet; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23229179

(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach which involves mixed feature sampling, sparse representation, and recognition. (2) Methods: This paper introduces a new method of identifying individuals through their ECG signals. This technique combines the extraction of fixed ECG features and specific frequency features to improve accuracy in ECG identity recognition. This approach uses the wavelet transform to extract frequency bands which contain personal information features from the ECG signals. These bands are reconstructed, and the single R-peak localization determines the ECG window. The signals are segmented and standardized based on the located windows. A sparse dictionary is created using the standardized ECG signals, and the KSVD (K-Orthogonal Matching Pursuit) algorithm is employed to project ECG target signals into a sparse vector–matrix representation. To extract the final representation of the target signals for identification, the sparse coefficient vectors in the signals are maximally pooled. For recognition, the co-dimensional bundle search method is used in this paper. (3) Results: This paper utilizes the publicly available European ST-T database for our study. Specifically, this paper selects ECG signals from 20, 50 and 70 subjects, each with 30 testing segments. The method proposed in this paper achieved recognition rates of 99.14%, 99.09%, and 99.05%, respectively. (4) Conclusion: The experiments indicate that the method proposed in this paper can accurately capture, represent and identify ECG signals.