Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches
Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/18/12/4138 |
| _version_ | 1798002499956768768 |
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| author | Kuo-Kun Tseng Jiao Lo Chih-Cheng Chen Shu-Yi Tu Cheng-Fu Yang |
| author_facet | Kuo-Kun Tseng Jiao Lo Chih-Cheng Chen Shu-Yi Tu Cheng-Fu Yang |
| author_sort | Kuo-Kun Tseng |
| collection | DOAJ |
| description | Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm. |
| first_indexed | 2024-04-11T11:54:17Z |
| format | Article |
| id | doaj.art-7dc47f83788243cba757f703b6564a96 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:54:17Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7dc47f83788243cba757f703b6564a962022-12-22T04:25:14ZengMDPI AGSensors1424-82202018-11-011812413810.3390/s18124138s18124138Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional ApproachesKuo-Kun Tseng0Jiao Lo1Chih-Cheng Chen2Shu-Yi Tu3Cheng-Fu Yang4School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Information Engineering, Jimei University, Xiamen 361021, ChinaMathematics Department, University of Michigan, Flint, Ann Arbor, MI 48502, USADepartment of Chemical and Materials Eng., N.U.K., Kaohsiung 81148, TaiwanElectrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.https://www.mdpi.com/1424-8220/18/12/4138quantization sparse matrixECGblood oxygenidentification |
| spellingShingle | Kuo-Kun Tseng Jiao Lo Chih-Cheng Chen Shu-Yi Tu Cheng-Fu Yang Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches Sensors quantization sparse matrix ECG blood oxygen identification |
| title | Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches |
| title_full | Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches |
| title_fullStr | Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches |
| title_full_unstemmed | Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches |
| title_short | Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches |
| title_sort | electrocardiograph identification using hybrid quantization sparse matrix and multi dimensional approaches |
| topic | quantization sparse matrix ECG blood oxygen identification |
| url | https://www.mdpi.com/1424-8220/18/12/4138 |
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