Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS
This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequenc...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
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Institute of Physics Publishing
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/81980/ http://irep.iium.edu.my/81980/1/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying.pdf http://irep.iium.edu.my/81980/2/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying_SCOPUS.pdf |
| _version_ | 1848789216966737920 |
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| author | Mohd Azam, Siti Nurfarah Ain Zohra, Fatema-tuz Sidek, Khairul Azami Smolen, Magdalena |
| author_facet | Mohd Azam, Siti Nurfarah Ain Zohra, Fatema-tuz Sidek, Khairul Azami Smolen, Magdalena |
| author_sort | Mohd Azam, Siti Nurfarah Ain |
| building | IIUM Repository |
| collection | Online Access |
| description | This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study. |
| first_indexed | 2025-11-14T17:53:12Z |
| format | Proceeding Paper |
| id | iium-81980 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:53:12Z |
| publishDate | 2020 |
| publisher | Institute of Physics Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-819802020-10-20T00:37:36Z http://irep.iium.edu.my/81980/ Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS Mohd Azam, Siti Nurfarah Ain Zohra, Fatema-tuz Sidek, Khairul Azami Smolen, Magdalena TK Electrical engineering. Electronics Nuclear engineering TK7885 Computer engineering This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study. Institute of Physics Publishing 2020-06-17 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/81980/1/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying.pdf application/pdf en http://irep.iium.edu.my/81980/2/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying_SCOPUS.pdf Mohd Azam, Siti Nurfarah Ain and Zohra, Fatema-tuz and Sidek, Khairul Azami and Smolen, Magdalena (2020) Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS. In: International Conference on Telecommunication, Electronic and Computer Engineering 2019, ICTEC 2019, 22nd - 24th Oct. 2019, Melaka, Malaysia.. https://iopscience.iop.org/article/10.1088/1742-6596/1502/1/012050 10.1088/1742-6596/1502/1/012050 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TK7885 Computer engineering Mohd Azam, Siti Nurfarah Ain Zohra, Fatema-tuz Sidek, Khairul Azami Smolen, Magdalena Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title | Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title_full | Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title_fullStr | Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title_full_unstemmed | Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title_short | Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS |
| title_sort | cardioid graph based ecg biometric in varying physiological conditions using compressed qrs |
| topic | TK Electrical engineering. Electronics Nuclear engineering TK7885 Computer engineering |
| url | http://irep.iium.edu.my/81980/ http://irep.iium.edu.my/81980/ http://irep.iium.edu.my/81980/ http://irep.iium.edu.my/81980/1/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying.pdf http://irep.iium.edu.my/81980/2/81980_Cardioid%20Graph%20Based%20ECG%20Biometric%20in%20Varying_SCOPUS.pdf |