BIOMETRIC SYSTEMS BASED ON ECG USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND VARIATIONAL MODE DECOMPOSITION
Keywords:
ECG, identification, VMD, EEMD.
Abstract
This study has simulated an ECG-based person identification system. This research is a development of previous studies in which accuracy improvement is the main focus to be achieved. One lead ECG signal from 11 subjects was simulated in this work. ECG signals from each person are then segmented into 10 windows to become training data and test data. Variational Mode Decomposition (VMD) and Ensemble Empirical Mode Decomposition (EEMD) methods are used to decompose ECG signals into 5 sub signals. Feature extraction based on statistical calculations was applied at each level of decomposition to obtain the characteristics of the ECG signal. Mean, variance, skewness, kurtosis and entropy have been evaluated as predictors. Support vector machines and 10-fold cross validation are used to validate the performance of the proposed method. Our simulations demonstrate that the proposed method outperforms several previous studies and achieves an accuracy of up to 98.2%.
References
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2. Nemirko;Lugovaya, “Biometric human identification based on electrocardiogram,” in Proc. XII-th Russian Conference on Mathematical Methods of Pattern Recognition, 2005, pp. 387–390.
3. L. Biel, O. Pettersson, L. Philipson, and P. Wide, “ECG analysis: A new approach in human identification,” IEEE Trans. Instrum. Meas., vol. 50, no. 3, pp. 808–812, 2001.
4. V. Krasteva, I. Jekova, and R. Schmid, “Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram,” PLoS One, vol. 13, no. 5, pp. 1–25, 2018.
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6. N. Belgacem, A. Nait-Ali, R. Fournier, and F. Bereksi-Reguig, “ECG Based Human Authentication using Wavelets and Random Forests,” Int. J. Cryptogr. Inf. Secur., vol. 2, no. 2, pp. 1–11, 2012.
7. W. Wei-quan, L. U. Pan, L. I. N. Jia-lun, and Z. Jin, “ECG Identification Based on Wavelet Transform,” no. Jimec, pp. 497–501, 2016.
8. A. Pal and Y. N. Singh, “Biometric recognition using area under curve analysis of electrocardiogram,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 1, pp. 533–545, 2019.
9. C. Carreiras, A. Lourenço, H. Silva, and A. Fred, “A unifying approach to ECG biometric recognition using the wavelet transform,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7950 LNCS, pp. 53–62, 2013.
10. M. N. Dar, M. U. Akram, A. Shaukat, and M. A. Khan, “ECG based biometric identification for population with normal and cardiac anomalies using hybrid HRV and DWT features,” 2015 5th Int. Conf. IT Converg. Secur. ICITCS 2015 - Proc., 2015.
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12. N. Belgacem, R. Fournier, A. Nait-Ali, and F. Bereksi-Reguig, “A novel biometric authentication approach using ECG and EMG signals,” J. Med. Eng. Technol., vol. 39, no. 4, pp. 226–238, 2015.
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14. D. Wang, Y. Si, W. Yang, G. Zhang, and J. Li, “A novel electrocardiogram biometric identification method based on temporal-frequency autoencoding,” Electron., vol. 8, no. 6, pp. 1–24, 2019.
15. I. Odinaka et al., “ECG biometrics: A robust short-time frequency analysis,” 2010 IEEE Int. Work. Inf. Forensics Secur. WIFS 2010, 2010.
16. J. S. Arteaga-Falconi, H. Al Osman, and A. El Saddik, “ECG Authentication for Mobile Devices,” IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 591–600, 2016.
17. R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, and F. Scotti, “Deep-ECG: Convolutional Neural Networks for ECG biometric recognition,” Pattern Recognit. Lett., vol. 126, pp. 78–85, 2019.
18. S. Hadiyoso, S. Aulia, and A. Rizal, “One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 2, pp. 276–283, 2019.
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20. S. Hadiyoso, A. Rizal, and I. Wijayanto, “ECG based biometric using wavelet packet decomposition,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 2178–2183, 2019.
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22. J. Parák and J. Havlík, “ECG signal processing and heart rate frequency detection methods,” Proc. Tech. Comput. Prague, no. January, 2011.
23. S. Yol, M. A. Özdemir, A. Akan, and L. F. Chaparro, “Detection of Epileptic Seizures by the Analysis of EEG Signals Using Empirical Mode Decomposition,” 2018 Med. Technol. Natl. Congr. TIPTEKNO 2018, pp. 1–4, 2018.
24. M. Salmanvandi and Z. Einalou, “Separation of twin fetal ECG from maternal ECG using empirical mode decomposition techniques,” Biomed. Eng. - Appl. Basis Commun., vol. 29, no. 6, pp. 1–12, 2017.
25. G. Liu and Y. Luan, “An adaptive integrated algorithm for noninvasive fetal ECG separation and noise reduction based on ICA-EEMD-WS,” Med. Biol. Eng. Comput., vol. 53, no. 11, pp. 1113–1127, 2015.
26. Z. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method,” Adv. Adapt. Data Anal., vol. 01, no. 01, pp. 1–41, Jan. 2009.
27. K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Trans. Signal Process., vol. 62, no. 3, pp. 531–544, 2014.
28. H. Ye, J. Zhu, Y. Cheng, D. Xue, B. Wang, and Y. Peng, “PPG based Respiration Signal Estimation using VMD-PCA,” in 2018 24th International Conference on Automation and Computing (ICAC), 2019, pp. 1–5.
29. K. Dragomiretskiy, D. Zosso, Variational mode decomposition, IEEE Trans. Signal Proc. 62 (3) (2014) 531–544.
30. C.~Cortes, C.~Cortes, V.~Vapnik, and V.~Vapnik, “Support Vector Networks,” Mach. Learn., vol. 20, no. 3, pp. 273~-~297, 1995.
31. R. Smíšek et al., “SVM Based ECG Classification Using Rhythm and Morphology Features, Cluster Analysis and Multilevel Noise Estimation,” vol. 44, pp. 1–4, 2017.
32. R. Smíšek, “ECG Signal Classification Based on SVM,” Biomed. Eng. (NY)., no. 1, pp. 365–369, 2016.
33. I. Wijayanto, A. Rizal, and S. Hadiyoso, “Multilevel Wavelet Packet Entropy and Support Vector Machine for Epileptic EEG Classification,” in 2018 4th International Conference on Science and Technology (ICST), 2018, pp. 9–14.
34. M. Mitha, S. S. Shiju, and M. Viswanadhan, “Automated epileptic seizure detection using relevant features in support vector machines,” 2014 Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT 2014, pp. 1000–1004, 2014.
35. E. Boser, N. Vapnik, I. M. Guyon, and T. B. Laboratories, “Training Algorithm Margin for Optimal Classifiers,” Perception, pp. 144–152, 1992.
