Home   >   CSC-OpenAccess Library   >    Manuscript Information
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic and AR Modeling Extracted Parameters
Branislav Vuksanovic, Mustafa Alhamdi
Pages - 25 - 42     |    Revised - 31-10-2015     |    Published - 30-11-2015
Volume - 9   Issue - 3    |    Publication Date - November 2015  Table of Contents
ECG Biometric, Filtering, QRS Detection, AR Model, Extraction and Classification.
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
CITED BY (1)  
1 Bassiouni, M., Khalefa, W., El-Dahshan, E. S. A., & Salem, A. B. M. (2015). A study on the Intelligent Techniques of the ECG-based Biometric Systems.Recent advances in electrical engineering, 26.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
I. Odinaka, A. Kaplan, J. O'Sullivan and L. Po-Hsiang (2012), ECG Biometric Recognition: A Comparative Analysis, IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1812 - 1824.
P. Hee-Soo and W. Soo-Min (2009), ECG Pattern Classification Based on Generic Feature Extraction, Proceedings of the 3rd WSEAS Int. Conf. on circuits, systems, signal and telecommunication, (CISST'09) ISSN:, vol. 21.
P. Tadejko and W. Rakowski (2007), Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification, 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM '07, pp. 322327.
A. Goldberger, L. Amaral, L. Glass and Hausdorff (2000), Components of a New Research Resource for Complex Physiologic Signals., PhysioBank, PhysioToolkit, and PhysioNet:, vol. 23, no. 21, pp. 101.
A. Ziarani and A. Konrad (2010), Non linear Adaptive method of elimination of power line interference in ECG signals, IEEE Transactions on Biomedical Eng, vol. 49, no. 6, pp. 540544.
B. Vuksanovic and M. Alhamdi (2013), ECG Based System for Arrythmia Detection and Patient Identification, ITI2013, pp. 1-7.
C. Alexakis, H. O. Nyongesa, R. Saatchi and N. D. Har (2007), Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycaemia, Conference on computers in Cardiology, pp. 53.
C. Anderson, E. Stolz and S. Shamsunder (1995), Discriminating Mental Tasks Using EEG Represented by AR Models, Engineering in Medicine and Biology Society, IEEE 17th Annual Conference , vol. 2.
D. Gari, A. Francisco and M. Patrick (2006), Biomedical Engineering. :Advanced Methods And Tools for ECG Data Analysis, Artech House, Inc.
D. Milano (1992), Politecnico Biosignals Archives on CD-ROM, (Copyright (C) Politecnico. 1992).
G. Kokturk (1998), A real-time simulated QRS detection system constructed using wavelet filtering technique, IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 281 - 284.
H.-Y. Zhou and K.-M. Hou (2008), Embedded real-time QRS detection algorithm for pervasive cardiac care system, 9th International Conference on Signal Processing, pp. 2150 - 2153.
I. Duskalov (1998), Developments in ECG acquisition, preprocessing, parameter measurement, and recording, IEEE Engineering in Medicine and Biology Magazine, vol. 7, no. 2, pp. 50 - 58.
I. Eisenstein, J. Edelstein and R. Sarma (1982), The electrocardiogram in obesity, J. Electrocardiol., vol. 15, no. 2, pp. 115118.
J. F. Moraes (2002), QRS complex detection algorithm using electrocardiogram leads, Computers in Cardiology, pp. 205 - 208.
J. Irvine, B. Wiederhold, L. Gavshon and S. Israel (2001), Heart rate variability: A new biometric for human identification, Proc. Int. Conf. Artificial Intelligence (ICAI 2001).
K. Jain, A. Ross and S. Prabhakar (2004), An introduction to biometric recognition, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20.
L. Biel, O. Pettersson, L. Philipson and P. Wide (2001), ECG analysis: A new approach in human identifications, IEEE Trans. Instrum. Meas., vol. 50, no. 33, pp. 808812.
M. Chavan, R. Aggarwala and M. Uplane (2008), Interference reduction in ECG using digital FIR filters based on Rectangular window, WSEAS Transactions on Signal Processing, vol. 4, no. 5, pp. 340-49.
M. Kyosoand and A. Uchiyama (2001), Developmentofan ECG identification system, in Proc. 23rd Ann. EMBS Int. Conf.
N. Uchaipichat and S. Inban (2010), Development of QRS Detection using Short-time Fourier Transform based Technique, IJCA Journal.
R. Hoekema and G. J. H. Uijen (2001), Geometrical aspects of the interindividual variability of multilead ECG recordings, IEEE Transactions on Biomedical Engineering, vol. 48, no. 5, pp. 551559.
R. Mark (1990), The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it, IEEE in Computers in Cardiology 1990, Proceedings.
S. A. Israel, J. M. Irvine and A. Cheng (2005), ECG to identify individuals, Pattern Recognition, pp. 133-142.
S. Dhillon and S. Chakrabarti (2001), Power Line Interference removal From Electrocardiogram Using A Simplified Lattice Based Adaptive IIR Notch Filter, Proceedings of the 23rd Annual EMBS International conference,Istanbul, Turkey.
S. K. Jagtap (2012), The impact of digital filtering to ECG analysis: Butterworth filter application, Communication, Information & Computing Technology (ICCICT), 2012 International Conference on, pp. 1-6.
S. Mahmoodabadi, A. Ahmadia and M. Abolhas (2005), ECG Feature Extraction using Daubechies Wavelets, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348.
S. Pooranchandra and N. Kumaravel (2010), A novel method for elimination of power line frequency in ECG signal using hyper shrinkage functions, Digital Signal Processing, vol. 18, no. 2, pp. 116-126.
S. Safie, J. Soraghan and L. Petropoulakis (2011), ECG biometric authentication using Pulse Active Width (PAW), Biometric Measurements and Systems for Security and Medical Applications (BIOMS), 2011 IEEE Workshop on, pp. 1 - 6.
S.-H. Lin (2000), An Introduction to Face Recognition Technology, Informing Science special Issue on Multimedia Informing Technologies , vol. 3, no. 1, pp. 1-7.
T. Shen and W. Tompkins (2002), One-lead ECG for identity verification, in: Proceedings of the 24th Annual International Conference of IEEE EMBS, pp. 62-63.
W. Liang, Y. Zhang and J. Tan (2009), A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks, Multidisciplinary Digital Publishing Institute, vol. 14, pp. 5994-6011,.
Y. L. Singh and P. Gupta (2008), ECG to Individual Identification, Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on, pp. 1 - 8.
Y. Wang, F. Agrafioti and D. Hatzinak (2008), Analysis of Human Electrocardiogram, Hindawi Publishing Corporation , EURASIP Journal on Advances in Signal Processing, pp. 11.
Dr. Branislav Vuksanovic
Faculty of technology/School of engineering University of Portsmouth Portsmouth, PO1 2UP, United Kingdom - United Kingdom
Dr. Mustafa Alhamdi
University of Portsmouth - United Kingdom