ECG based personal identification using extended Kalman filter

This paper proposes a new approach for electrocardiogram (ECG) based personal identification based on extended Kalman filtering (EKF) framework. The framework uses nonlinear ECG dynamic models formulated to represent noisy ECG signal. The advantage of the models is the ability to capture distinct ECG features used for biometric recognition such as temporal and amplitude distances between PQRST points. Moreover the inherent modeling of additive noise provides robust recognition. Log-likelihood scoring is proposed for classification. The algorithm is evaluated on identification task on 13 subjects of MIT-BIH Arrhythmia Database using single lead data. Identification rate of 87.50% is achieved on 30s test recordings of normal beat. Experimental results using artificial additive white noise show that the model is robust to noise for SNR level above 20dB.

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

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

[3]  Bernadette Dorizzi,et al.  ECG signal analysis through hidden Markov models , 2006, IEEE Transactions on Biomedical Engineering.

[4]  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).

[5]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

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

[7]  Ali H. Shoeb,et al.  Model-based filtering, compression and classification of the ECG , 2005 .

[8]  Mohammad Bagher Shamsollahi,et al.  ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure , 2008, IEEE Transactions on Biomedical Engineering.

[9]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[10]  T. Westerlund,et al.  Remarks on "Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems" , 1980 .

[11]  A. van Oosterom,et al.  Geometrical aspects of the inter-individual variability of multilead ECG recordings , 1999, Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004).

[12]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .