ECG fiducial points extraction by extended Kalman filtering

Most of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was constructed. Quantitative and qualitative evaluations of the proposed method have been done on Physionet QT database (QTDB). This method is also compared with a method based on Partially Collapsed Gibbs Sampler (PCGS). Results show that the proposed method can detect fiducial points of ECG precisely and mean of estimation error of all FPs (except Ton) do not exceed five samples (20 msec).

[1]  M B Shamsollahi,et al.  A model-based Bayesian framework for ECG beat segmentation , 2009, Physiological measurement.

[2]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[3]  K. Chan,et al.  Characteristic wave detection in ECG signal using morphological transform , 2005, BMC cardiovascular disorders.

[4]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[5]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[6]  Jean-Yves Tourneret,et al.  P and twave delineation andwaveform estimation in ECG signals using a block gibbs sampler , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

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

[9]  Jean-Yves Tourneret,et al.  P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler , 2010, IEEE Transactions on Biomedical Engineering.

[10]  Nicholas Peter Hughes,et al.  Probabilistic Models for Automated ECG Interval Analysis , 2006 .

[11]  Christian Jutten,et al.  ECG denoising using angular velocity as a state and an observation in an Extended Kalman Filter framework , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Mohammad Bagher Shamsollahi,et al.  Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework , 2010, IEEE Transactions on Biomedical Engineering.

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

[14]  K.Venkatesh Prasad,et al.  Fundamentals of statistical signal processing: Estimation theory: by Steven M. KAY; Prentice Hall signal processing series; Prentice Hall; Englewood Cliffs, NJ, USA; 1993; xii + 595 pp.; $65; ISBN: 0-13-345711-7 , 1994 .

[15]  Christian Jutten,et al.  Fiducial points extraction and characteristicwaves detection in ECG signal using a model-based bayesian framework , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.