ECG based personal identification using extended Kalman filter

Chee Ming Ting, Sh Hussain Salleh

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40 Citations (Scopus)


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.

Original languageEnglish
Title of host publication10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Number of pages4
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Information Science, Signal Processing and their Applications 2010 - Kuala Lumpur, Malaysia
Duration: 10 May 201013 May 2010
Conference number: 10th (Proceedings)


ConferenceInternational Conference on Information Science, Signal Processing and their Applications 2010
Abbreviated titleISSPA 2010
CityKuala Lumpur
Internet address


  • Electrocardiography
  • Identification of persons
  • Kalman filtering

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