ECG biometric recognition using SVM-based approach
This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty‐one features based on temporal and amplitude dis...
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| Published in | IEEJ transactions on electrical and electronic engineering Vol. 11; no. S1; pp. S94 - S100 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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01.06.2016
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| Abstract | This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty‐one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT‐BIH Arrhythmia and MIT‐BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
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| AbstractList | This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty‐one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT‐BIH Arrhythmia and MIT‐BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty-one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT-BIH Arrhythmia and MIT-BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia. copyright 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
| Author | Lachiri, Zied Rezgui, Dhouha |
| Author_xml | – sequence: 1 givenname: Dhouha surname: Rezgui fullname: Rezgui, Dhouha email: rezgui_dhouha@yahoo.com organization: Department of Electrical Engineering, National Engineering School of Tunis, University of Tunis El Manar, Tunisia, Tunis 1002 – sequence: 2 givenname: Zied surname: Lachiri fullname: Lachiri, Zied organization: Department of Electrical Engineering, National Engineering School of Tunis, University of Tunis El Manar, Tunisia, Tunis 1002 |
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| References_xml | – reference: Biel L, Pettersson O, Philipson L, Wide P. ECG analysis: a new approach in human identification. IEEE Transactions on Instrumentation and Measurement 2001; 50(3):808-812. – reference: Lourenço A, Silva H, Fred A. Unveiling the biometric potential of finger-based ECG signals. Computational Intelligence and Neuroscience 2011; 2011:1-8. – reference: Irvine JM, Israel SA, Scruggs WT, Worek WJ. EigenPulse: robust human identification from cardiovascular function. Pattern Recognition 2008; 41(11):3427-343. – reference: Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998; 2:121-167. – reference: Chan ADC, Hamdy MM, Badre A, Badee V. Wavelet distance measure for person identification using electrocardiograms. IEEE Transactions on Instrumentation and Measurement 2008; 57:248-253. – reference: Jones SA. ECG Success: Exercises in ECG Interpretation. F. A. Davis Company: Philadelphia; 2008. – reference: Shen TW, Tompkins WJ, Hu YH. One-lead ECG for identity verification. Proceedings of the 2nd Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society 2002; 1:62-63. – reference: Clifford GD, Azuaje F, McSharry PE. Advanced Methods and Tools for ECG Data Analysis. Artech House, Inc: Boston; 2006. – reference: Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101(23):e215-e220. – reference: Singh YN, Gupta P. Correlation-based classification of heartbeats for individual identification. Soft Computing 2011; 15(3):449-460. – reference: Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK. ECG to identify individuals. Pattern Recognition 2005; 38(1):133-142. – reference: Christov I, Herrero GG, Krasteva V, Jekova I, Gotchev A, Egiazarian K. Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Medical Engineering & Physics 2006; 28(9):876-887. – reference: Singh YN, Gupta P. Biometrics method for human identification using electrocardiogram. Advances in Biometrics 2009; 5558:1270-1279. – reference: Hoekema R, Uijen GJH, van Oosterom A. Geometrical aspects of the interindividual variability of multilead ECG recordings. IEEE Transactions on Biomedical Engineering 2001; 48(5):551-559. – volume: 101 start-page: e215 issue: 23 year: 2000 end-page: e220 article-title: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals publication-title: Circulation – start-page: 1 year: 2005 end-page: 4 – volume: 15 start-page: 449 issue: 3 year: 2011 end-page: 460 article-title: Correlation‐based classification of heartbeats for individual identification publication-title: Soft Computing – volume: 1 start-page: 62 year: 2002 end-page: 63 article-title: One‐lead ECG for identity verification publication-title: Proceedings of the 2nd Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society – start-page: 1 year: 2008 end-page: 8 – volume: 48 start-page: 551 issue: 5 year: 2001 end-page: 559 article-title: Geometrical aspects of the interindividual variability of multilead ECG recordings publication-title: IEEE Transactions on Biomedical Engineering – volume: 2 start-page: 121 year: 1998 end-page: 167 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Mining and Knowledge Discovery – year: 2002 – volume: 38 start-page: 133 issue: 1 year: 2005 end-page: 142 article-title: ECG to identify individuals publication-title: Pattern Recognition – year: 2008 – start-page: 3721 year: 2001 end-page: 3723 – year: 2006 – volume: 5558 start-page: 1270 year: 2009 end-page: 1279 article-title: Biometrics method for human identification using electrocardiogram publication-title: Advances in Biometrics – start-page: 226 year: 2003 end-page: 231 – volume: 50 start-page: 808 issue: 3 year: 2001 end-page: 812 article-title: ECG analysis: a new approach in human identification publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 57 start-page: 248 year: 2008 end-page: 253 article-title: Wavelet distance measure for person identification using electrocardiograms publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 41 start-page: 3427 issue: 11 year: 2008 end-page: 343 article-title: EigenPulse: robust human identification from cardiovascular function publication-title: Pattern Recognition – volume: 28 start-page: 876 issue: 9 year: 2006 end-page: 887 article-title: Comparative study of morphological and time‐frequency ECG descriptors for heartbeat classification publication-title: Medical Engineering & Physics – start-page: 446 year: 2009 end-page: 451 – start-page: 190 year: 2003 end-page: 191 – volume: 2011 start-page: 1 year: 2011 end-page: 8 article-title: Unveiling the biometric potential of finger‐based ECG signals publication-title: Computational Intelligence and Neuroscience – start-page: 201 year: 2008 end-page: 206 – ident: e_1_2_8_12_1 doi: 10.1109/BTAS.2008.4699343 – ident: e_1_2_8_26_1 doi: 10.1109/IDAACS.2009.5342942 – ident: e_1_2_8_8_1 doi: 10.1109/19.930458 – ident: e_1_2_8_20_1 – ident: e_1_2_8_2_1 doi: 10.1016/j.patcog.2008.04.015 – volume-title: Advanced Methods and Tools for ECG Data Analysis year: 2006 ident: e_1_2_8_18_1 – ident: e_1_2_8_10_1 doi: 10.1109/IEMBS.2001.1019645 – ident: e_1_2_8_3_1 doi: 10.1109/APBME.2003.1302648 – volume: 1 start-page: 62 year: 2002 ident: e_1_2_8_9_1 article-title: One‐lead ECG for identity verification publication-title: Proceedings of the 2nd Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society – ident: e_1_2_8_21_1 doi: 10.1109/BCC.2006.4341627 – ident: e_1_2_8_6_1 doi: 10.1109/TIM.2007.909996 – ident: e_1_2_8_11_1 doi: 10.1155/2011/720971 – ident: e_1_2_8_7_1 doi: 10.1016/j.patcog.2004.05.014 – volume-title: ECG Success: Exercises in ECG Interpretation year: 2008 ident: e_1_2_8_17_1 – ident: e_1_2_8_23_1 doi: 10.1016/j.medengphy.2005.12.010 – ident: e_1_2_8_5_1 doi: 10.1109/MUE.2008.67 – ident: e_1_2_8_25_1 doi: 10.1109/TENCON.2005.300986 – ident: e_1_2_8_19_1 doi: 10.1161/01.CIR.101.23.e215 – ident: e_1_2_8_16_1 – ident: e_1_2_8_13_1 doi: 10.1007/978-3-642-01793-3_128 – ident: e_1_2_8_14_1 doi: 10.1007/s00500-009-0525-y – ident: e_1_2_8_24_1 doi: 10.1023/A:1009715923555 – ident: e_1_2_8_4_1 doi: 10.1109/10.918594 – ident: e_1_2_8_15_1 – ident: e_1_2_8_22_1 doi: 10.1109/AIPR.2003.1284276 |
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| SubjectTerms | amplitude attributes Arrhythmia Biometrics electrocardiogram (ECG) Electronic engineering Gaussian Kernels MIT-BIH database morphological descriptors Polynomials Recognition support vector machine Support vector machines temporal attributes |
| Title | ECG biometric recognition using SVM-based approach |
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