ECG Biometric Verification by using PCA
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Dr.Jameel Kadhim Abed, Nahrain N. Abd
|DOI : 10.14445/22315381/IJETT-V65P202|
MLA Style: Dr.Jameel Kadhim Abed, Nahrain N. Abd "ECG Biometric Verification by using PCA" International Journal of Engineering Trends and Technology 65.1 (2018): 4-8.
APA Style:Dr.Jameel Kadhim Abed, Nahrain N. Abd (2018). ECG Biometric Verification by using PCA. International Journal of Engineering Trends and Technology, 65(1), 4-8.
Identity verification faces several challenges, mainly in extracting unique features of individual biometric methods and classifications. For example, the forms of electric waves of the heart characterized by unique characteristics of human recognition and its reference are not periodic at ready, in order to generate a distinct set of ECG methods, which are used autocorrelation (AC) development in conjunction with dimension reduction methods. This writing paper suggests a new non-fiducial frame for an ECG biometric recognition using (PCA) to the reduction both of the dimensions high- vector autocorrelation vector and the verification system after noise removal of 8 individuals use Discrete Wavelet Transform (DWT).
 Gawande PS & Ladhake. SA, "An Impact of Different Feature Extraction Methods on Classification of Electrocardiogram" International Journal of Advanced Research in Computer Science and Software Engineering 5(7), July- 2015, vol. 5, pp. 667-970.
 K.N. Plataniotis, D. Hatzinakos and J.K.M. Lee., ECG biometric recognition without fiducial detection. In Proceedings of the 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, Baltimore, MD, USA, 21 August–19 September 2006, pp 1–6.
 O. Muñoz-Ramos, O. Starostenko, V,.Alarcon-Aquino and C. Cruz-Perez, Chapter 28 Real-Time System for Monitoring and Analyzing Electrocardiogram on Cell Phone. In: Elleithy, K.; Sobh, T. (eds) Innovations and Advances in Computer, Information, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering Springer, New York, (2013), vol. 152, pp. 327-338.
 P. Laguna, R. Jan6, E Bogatell and D.V. Anglada, (2008, Jun 12). QRS detection and waveform boundary recognition using ecgpuwave. Retrieved June 2010, from Physio Toolkit: http://www.physionet.org/physiotools/ecgpuwave/.
 The MIT-BIH Normal Sinus Rhythm Database. Available online: https://www.physionet.org/physiobank/database/nsrdb/ (accessed on 10 October 2017).
 The PTB Diagnostic ECG Database. Available online: https://www.physionet.org/physiobank/database/ptbdb/ (accessed on 10 October 2017).
 The QT Database. Available online: https:// physionet.org /physiobank/database/qtdb/ (accessed on 10 October 2017).
 N. Kamal, AH. Mohammad, I. Ramin & BM. Thomas, Chapter 13 Contact-Free Heartbeat Signal for Human Identification and Forensics". Handbook of Biometrics for Forensic Science Advances in Computer Vision and Pattern Recognition, Springer AG 2017, pp. 289-301.
 C. Matteo, M. Jesper, M. Anne-Birgitte & BDS. Helge, "A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation". IEEE Journal of Translational Engineering in Health and Medicine, 4 July 2017, vol. 5, pp. 1-15.
 J. Yao & Y. Wan, (2008). A Wavelet Method for Biometric Identification Using Wearable ECG Sensors. Proceedings of the 2008 5th International Workshop on Wearable and Implantable Body Sensor Networks, in conjunction with The 5th International Summer School and Symposium on Medical Devices and Biosensors, pp. 297-300 , Hong Kong, P.R.China, June 1-3, 2008, Retrieved (July 27th 2011) from Available from: http ://www.intechopen.com/ books/recentapplication-in-biometrics/biometrics-on-mobile-phone/, pp. 1-21,No. 65.
 MA. Mohamed & MA. Deriche, An Approach for ECG Feature Extraction using Daubechies4 (DB4) Wavelet. Int. J. Compute. Appl. 2014, vol. 96, pp. 36–41.
 J. Woo-Hyuk & L. Sang-Goog, "ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method". Appl. Sci, 22 November 2017, pp. 1-14.
 O. Boumbarov, Y. Velchev & S. Sokolov,”ECG personal identification in subspaces using radial basis neural networks,” IEEE Int. Workshop on Intelligent Data Acquisition and Advanced Computing Systems, 2009, pp. 446 –451.
 HF. Liau & D. Isa, Feature selection for support vector machine-based face-iris multimodal biometric system. Expert Syst. Appl. 2011, vol. 38, No. 9, pp. 11105–11111.
 L. Yang, G. Yang, Y. Yin & X. Xi, Exploring soft biometric trait with finger vein recognition. Neurocomputing 2014, vol. 135, No. 5, pp. 218–228.
Electro Cardio Gram (ECG) biometric recognition, Discrete Wavelet Transform (DWT), non-fiducial feature extraction, Auto Correlation (AC) and Principal Component Analysis (PCA).