Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation
(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, thi...
| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2023-11-01
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| coleção: | Sensors |
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| Acesso em linha: | https://www.mdpi.com/1424-8220/23/22/9179 |
| _version_ | 1827638758644645888 |
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| author | Xu Zhang Qifeng Liu Dong He Hui Suo Chun Zhao |
| author_facet | Xu Zhang Qifeng Liu Dong He Hui Suo Chun Zhao |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | (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. |
| first_indexed | 2024-03-09T16:28:22Z |
| format | Article |
| id | doaj.art-5344faffae0848a68b1569d08e7e0abd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:22Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5344faffae0848a68b1569d08e7e0abd2023-11-24T15:05:38ZengMDPI AGSensors1424-82202023-11-012322917910.3390/s23229179Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse RepresentationXu Zhang0Qifeng Liu1Dong He2Hui Suo3Chun Zhao4State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130015, ChinaSchool of Preparatory Education, Jilin University, Changchun 130015, ChinaState Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130015, ChinaState Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130015, ChinaState Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130015, China(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.https://www.mdpi.com/1424-8220/23/22/9179electrocardiogram (ECG)biometricwaveletsparse codingdictionary learning |
| spellingShingle | Xu Zhang Qifeng Liu Dong He Hui Suo Chun Zhao Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation Sensors electrocardiogram (ECG) biometric wavelet sparse coding dictionary learning |
| title | Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation |
| title_full | Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation |
| title_fullStr | Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation |
| title_full_unstemmed | Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation |
| title_short | Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation |
| title_sort | electrocardiogram based biometric identification using mixed feature extraction and sparse representation |
| topic | electrocardiogram (ECG) biometric wavelet sparse coding dictionary learning |
| url | https://www.mdpi.com/1424-8220/23/22/9179 |
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