Research on ECG Biometric in Cardiac Irregularity Conditions

This paper studies the principle of ECG signals applied to identification, particularly considers the case of users' ECG abnormal conditions. This paper presents an improved multi-template matching algorithm for identification, which can achieve good discrimination effects under ECG abnormality. Normal and abnormal ECG templates are constructed by QRS complex, the discrimination is based on the correlation coefficient of the testing data and template. We used 44 ECG data files from the MIT-BIH Arrhythmia Database (MITDB) to measure the performance of the algorithm, extracted normal templates in 18 data files as well as normal and abnormal templates in the remaining 26 data files. The experiment obtained an 88.06% accuracy of template matching, when considering the discrimination results of all the testing data belong to one user, the individual recognition accuracy reaches 100%. Experiments showed that the improved multi-template matching algorithm characterized by QRS complex can be used to identify individuals in the state of arrhythmia.

[1]  Dimitrios Hatzinakos,et al.  Analysis of Human Electrocardiogram for Biometric Recognition , 2008, EURASIP J. Adv. Signal Process..

[2]  Yu Hen Hu,et al.  One-lead ECG for identity verification , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[3]  Ibrahim Khalil,et al.  Person identification in irregular cardiac conditions using electrocardiogram signals , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  A. Amann,et al.  Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators , 2005, Biomedical engineering online.

[5]  Norlaili Mat Safri,et al.  A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation , 2013, Biomed. Signal Process. Control..

[6]  B P Simon,et al.  An ECG classifier designed using modified decision based neural networks. , 1997, Computers and biomedical research, an international journal.

[7]  Adrian D. C. Chan,et al.  Person Identification using Electrocardiograms , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[8]  Dimitrios Hatzinakos,et al.  ECG Based Recognition Using Second Order Statistics , 2008, 6th Annual Communication Networks and Services Research Conference (cnsr 2008).

[9]  Bin Liu,et al.  Normalizing Electrocardiograms of Both Healthy Persons and Cardiovascular Disease Patients for Biometric Authentication , 2013, PloS one.

[10]  Adriaan van Oosterom,et al.  Geometrical aspects of the interindividual variability of multilead ECG recordings , 2001, IEEE Transactions on Biomedical Engineering.

[11]  Ibrahim Khalil,et al.  Identification of Cardiac Autonomic Neuropathy patients using Cardioid based graph for ECG biometric , 2011, 2011 Computing in Cardiology.

[12]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[13]  Hany Selim,et al.  Human identification using time normalized QT signal and the QRS complex of the ECG , 2010, 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010).

[14]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[15]  Dimitrios Hatzinakos,et al.  ECG biometric analysis in cardiac irregularity conditions , 2009, Signal Image Video Process..

[16]  H A Fozzard,et al.  AZTEC, a preprocessing program for real-time ECG rhythm analysis. , 1968, IEEE transactions on bio-medical engineering.

[17]  Yongjin Wang,et al.  Integrating Analytic and Appearance Attributes for Human Identification from ECG Signals , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.