An intelligent model for ECG Classification System based on frequency domain with Least Square Support Vector Machine (LS-SVM)
DOI:
https://doi.org/10.29304/jqcm.2022.14.2.966Keywords:
ECG, authentication, frequency, feature, LSSVMAbstract
The development of authentication and identity mechanisms has become a vital requirement to secure device data integrity, although passwords provide sufficient control and authentication, they have shown major flaws in speed and security, Biometrics is becoming the primary authentication method. Electrocardiogram (ECG) signals are created as a consequence. Due to their unique character, which makes them difficult to falsify and ubiquitous, ECG signals have attracted much attention in most authentication systems. We introduce a novel ECG validation model that combines frequency domain-based characteristics with the least-squares technique in this work (LS-SVM). Two types of frequency field characteristics were examined in order to find the best combination of high precision and speed. ECG Signal Characteristics and Frequency Domain Characteristics ECG signals are used to determine the optimal triple-band filter bank. To access the most important characteristics, we removed the extraneous features and extracted others.
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References
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[3] T. Bai et al., “A lightweight method of data encryption in BANs using electrocardiogram signal,” Futur. Gener. Comput. Syst., vol. 92, pp. 800–811, 2019.
[4] P. Peris-Lopez, L. González-Manzano, C. Camara, and J. M. de Fuentes, “Effect of attacker characterization in ECG-based continuous authentication mechanisms for Internet of Things,” Futur. Gener. Comput. Syst., vol. 81, pp. 67–77, 2018.
[5] S. Hamza and Y. Ben Ayed, “Svm for human identification using the ecg signal,” Procedia Comput. Sci., vol. 176, pp. 430–439, 2020.
[6] S. Hong, Y. Zhou, J. Shang, C. Xiao, and J. Sun, “Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review,” Comput. Biol. Med., vol. 122, p. 103801, 2020.
[7] S. Hamza and Y. Ben Ayed, “SVM for human identification using the ECG signal,” Procedia Comput. Sci., vol. 176, pp. 430–439, 2020, doi: 10.1016/j.procs.2020.08.044.
[8] N. Rabinezhadsadatmahaleh and T. Khatibi, “A novel noise-robust stacked ensemble of deep and conventional machine learning classifiers (NRSE-DCML) for human biometric identification from electrocardiogram signals,” Informatics Med. Unlocked, vol. 21, p. 100469, 2020, doi: 10.1016/j.imu.2020.100469.
[9] W. Klonowski, “Fractal analysis of electroencephalographic time series (EEG Signals),” in The fractal geometry of the brain, Springer, 2016, pp. 413–429.
[10] H. Namazi and S. Jafari, “Age-based variations of fractal structure of EEG signal in patients with epilepsy,” Fractals, vol. 26, no. 04, p. 1850051, 2018.
[11] B. Hjorth, “EEG analysis based on time domain properties,” Electroencephalogr. Clin. Neurophysiol., vol. 29, no. 3, pp. 306–310, 1970.
[12] E. Al Alkeem et al., “Robust Deep Identification using ECG and Multimodal Biometrics for Industrial Internet of Things,” Ad Hoc Networks, vol. 121, no. May, p. 102581, 2021, doi: 10.1016/j.adhoc.2021.102581.
[13] J. S. Arteaga-Falconi, H. Al Osman, and A. El Saddik, “ECG and fingerprint bimodal authentication,” Sustain. Cities Soc., vol. 40, no. August 2017, pp. 274–283, 2018, doi: 10.1016/j.scs.2017.12.023.
[14] K. Wang, G. Yang, Y. Huang, and Y. Yin, “Multi-scale differential feature for ECG biometrics with collective matrix factorization,” Pattern Recognit., vol. 102, p. 107211, 2020, doi: 10.1016/j.patcog.2020.107211.
[15] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45–50, 2001.
[16] S. V Stehman, “Selecting and interpreting measures of thematic classification accuracy,” Remote Sens. Environ., vol. 62, no. 1, pp. 77–89, 1997.
[17] D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv Prepr. arXiv2010.16061, 2020.
[18] C. Sammut and G. I. Webb, Encyclopedia of machine learning. Springer Science & Business Media, 2011.
[19] H. Brooks et al., “WWRP/WGNE Joint Working Group on Forecast Verification Research,” Collab. Aust. Weather Clim. Res. World Meteorol. Organ., 2015.
[20] S. K. D. Alkhafaji, “Evaluation of the influence of directivity factor of directive elements of conformal and planar antenna arrays on the performances of azimuth-elevation DOA estimation,” in Journal of Physics: Conference Series, 2019, vol. 1279, no. 1, p. 12024.
[21] Y. B. Nechaev, K. D. A. Sarmad, and I. W. Peshkov, “Evaluating expectation-maximization algorithm for 2D DOA estimation via planar antenna arrays,” in Proceedings of the International Conference on High Performance Compilation, Computing and Communications, 2017, pp. 126–130.
[22] M. Diykh, S. Abdulla, K. Saleh, and R. C. Deo, “Fractal dimension undirected correlation graph-based support vector machine model for identification of focal and non-focal electroencephalography signals,” Biomed. Signal Process. Control, vol. 54, p. 101611, 2019.
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Published
2022-07-06
How to Cite
Majeed, R. R., & Kadhim, S. (2022). An intelligent model for ECG Classification System based on frequency domain with Least Square Support Vector Machine (LS-SVM). Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(2), Comp Page 46–58. https://doi.org/10.29304/jqcm.2022.14.2.966
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Computer Articles
