SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG
The demand for reliable identification systems has grown recently. Using the mean frequency, median frequency, band power, and Welch power spectral density (PSD) of ECG data, we proposed a novel biometric approach in this study. ECG signals are more secure than other traditional biometric modalitie...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2023-05-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/373 |
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| _version_ | 1823858351601090560 |
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| author | HATEM ZEHIR TOUFIK HAFS SARA DAAS AMINE NAIT-ALI |
| author_facet | HATEM ZEHIR TOUFIK HAFS SARA DAAS AMINE NAIT-ALI |
| author_sort | HATEM ZEHIR |
| collection | DOAJ |
| description |
The demand for reliable identification systems has grown recently. Using the mean frequency, median frequency, band power, and Welch power spectral density (PSD) of ECG data, we proposed a novel biometric approach in this study. ECG signals are more secure than other traditional biometric modalities because they are impossible to forge and duplicate. Three different support vector machine classifiers—linear SVM, quadratic SVM, and cubic SVM—are employed for the classification. The MIT-BIH arrhythmia database is used to evaluate the suggested method's precision. For the linear SVM, quadratic SVM, and cubic SVM, respectively, test accuracy of 93.6%, 96.4%, and 97.0% was obtained.
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| format | Article |
| id | doaj-art-8ae4bd9a706a42c3b109b04eb03fd030 |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-8ae4bd9a706a42c3b109b04eb03fd0302025-02-11T11:39:22ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322023-05-01291SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECGHATEM ZEHIRTOUFIK HAFSSARA DAASAMINE NAIT-ALI The demand for reliable identification systems has grown recently. Using the mean frequency, median frequency, band power, and Welch power spectral density (PSD) of ECG data, we proposed a novel biometric approach in this study. ECG signals are more secure than other traditional biometric modalities because they are impossible to forge and duplicate. Three different support vector machine classifiers—linear SVM, quadratic SVM, and cubic SVM—are employed for the classification. The MIT-BIH arrhythmia database is used to evaluate the suggested method's precision. For the linear SVM, quadratic SVM, and cubic SVM, respectively, test accuracy of 93.6%, 96.4%, and 97.0% was obtained. https://jesr.ub.ro/index.php/1/article/view/373biometrics, hidden biometrics, security, identification, ECG, machine learning, SVM |
| spellingShingle | HATEM ZEHIR TOUFIK HAFS SARA DAAS AMINE NAIT-ALI SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG Journal of Engineering Studies and Research biometrics, hidden biometrics, security, identification, ECG, machine learning, SVM |
| title | SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG |
| title_full | SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG |
| title_fullStr | SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG |
| title_full_unstemmed | SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG |
| title_short | SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG |
| title_sort | support vector machine for human identification based on non fiducial features of the ecg |
| topic | biometrics, hidden biometrics, security, identification, ECG, machine learning, SVM |
| url | https://jesr.ub.ro/index.php/1/article/view/373 |
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